Papers

Parampreet Singh, Anushka Singh, Sumit Kumar et al.

TCP_α provides mathematically-guaranteed separation between confidence scores for correct vs. incorrect predictions, enabling reliable failure detection—rejecting just 8% of least-confident predictions improved music classification from 89% to 98% accuracy.

This paper proposes TCP_α, a new method for training neural networks to reliably estimate their own confidence in predictions. Unlike existing approaches that struggle to distinguish between correct and incorrect predictions, TCP_α uses a margin-based penalty that mathematically guarantees clear separation between confidence scores for right and wrong answers.

Anton Lambrecht, Reda El Hail, Xianjun Jiao et al.

Different RF sensing technologies excel at different things: IR-UWB wins on accuracy, FMCW on generalization to new spaces. For healthcare monitoring, choose based on whether you prioritize activity recognition or robustness to environmental changes.

This paper compares three radar technologies (FMCW, IR-UWB, and Wi-Fi) for detecting human activities and sleep patterns from ceiling-mounted sensors. Using the same neural network and test conditions across 20 people and different room layouts, the study shows IR-UWB performs best overall (89% accuracy), while FMCW adapts better to new environments.

Narges Ahmadi, Yubo Jiao, Jônatas Augusto Manzolli et al.

Large language models can match or exceed traditional machine learning for travel behavior prediction without task-specific training, and adding visual context from survey images improves performance—showing that multimodal AI can enhance behavioral modeling when integrated with human-centered ...

This paper presents a three-agent workflow that combines chatbot surveys, data processing, and prediction to model how weather affects commuter mode choices. The system collected 454 survey responses about travel preferences across different weather scenarios, then compared traditional statistical models with nine different large language models (2-35B parameters) for predicting travel behavior.

Yucheng Jiang, Zora Zhiruo Wang, Ruishi Chen et al.

By converting raw computer activity into structured task models with goal hierarchies and control flow, TMI enables AI agents to learn realistic work procedures and organizations to audit and reuse task knowledge from employee activity traces.

This paper presents Task Model Induction (TMI), a method that automatically discovers and structures how people actually work on computers by analyzing screenshots and input logs.

Yizhe Chi, Wenyi Li, Deyao Hong et al.

Current LLM agents struggle to design better training algorithms: even the best system closes only 20% of the gap between existing algorithms and optimal ones, suggesting recursive self-improvement through algorithmic design remains far out of reach.

This paper introduces AI4AI-Bench, a benchmark that tests whether AI agents can improve training algorithms themselves—a key capability for recursive self-improvement. The benchmark uses 10 frozen code repositories where agents get 4 hours to rewrite training algorithms, then the code runs for 12 hours and is scored against the original.

Adam Fisch, Shubhendu Trivedi, Fantine Huot et al.

Use value-of-information theory to decide when to invest in expensive model quality estimates before routing—this cuts estimation costs dramatically while maintaining routing accuracy.

This paper solves the problem of efficiently routing queries to the best AI model in a system with multiple specialists. The key challenge: estimating which model will perform best costs money (slow but accurate estimators vs. fast but noisy ones).

Jun Ni Du, Lukas Adamek, Maxim Kryukov et al.

This work shows how to build interpretable clinical prediction models from structured EHR data by combining effective lab value encoding with attribution methods that clinicians can understand and trust.

BERT-LER is a machine learning model designed to predict health outcomes from patient medical records while explaining its decisions. It represents lab test results as discrete tokens with percentile-based information and uses a technique called Integrated Gradients to show which medical events influenced each prediction.

Fengqing Jiang, Yite Wang, Boyi Liu et al.

Tool-use capabilities in language models improve significantly when trained during mid-training with targeted synthetic data, rather than waiting until post-training—similar to how math and reasoning skills benefit from dedicated training phases.

MidTool is a data synthesis pipeline that creates training data for teaching language models to use tools effectively during mid-training (the stage between pretraining and fine-tuning).

Guan-Ju Peng

Highly coherent learned dictionaries can assign different physical meanings to the same sparse representation; you need to account for dictionary uncertainty and check which physical interpretations survive across all calibration-compatible alternatives.

When learning dictionaries from data to represent signals sparsely, the selected atoms may seem physically meaningful but could be arbitrary artifacts of the calibration data—especially when many different dictionaries fit equally well.

Cheng Xu, Nan Yan, Liming Chen et al.

When evaluating whether models improve on individual problems, you need a separately measured null baseline for every statistic—not just comparing two noisy estimates—or you'll mistake measurement artifacts for real capability gains.

This paper audits claims about language model self-improvement by comparing a fine-tuned model against a frozen control run through identical evaluation pipelines. The authors identify seven measurement artifacts that flip reported findings, showing that many apparent capability gains are statistical illusions from batching effects and noisy comparisons rather than real improvements.

Qian Kou, Xiaofeng Shi, Xiaosong Qiu et al.

To internalize document knowledge without retrieval, separate the training into three stages: inject documents as training data, align with QA supervision, then merge back with the base model to avoid catastrophic forgetting of general skills.

This paper tackles the problem of making LLMs answer questions about specific documents without retrieving them at inference time. The authors propose IAR, a three-stage training method that injects document knowledge, aligns the model to answer questions correctly, and recovers general abilities by merging with the base model.

Yash Kulkarni, Shubham Harkare, Arvind Suresh Yogesh Babu

For LLM semantic caches, focus on validating answer quality before optimizing eviction policy; LFU is a solid default, and geometry-aware policies don't help much because newly cached queries rarely have similar neighbors.

This paper evaluates different cache eviction policies for semantic caches that store LLM responses based on query embeddings. Testing seven policies across multiple datasets and cache sizes, the authors find that LFU (Least Frequently Used) performs best, but no policy significantly outperforms it.

Yiyang Feng, Biddut Sarker Bijoy, Niranjan Balasubramanian et al.

Subtask-level skills expressed as text transfer more reliably across tasks than task-level or code-based skills, and you can predict skill usefulness by balancing how specific it is versus how broadly applicable it is.

This paper studies how LLM agents can learn and reuse skills from completed tasks. The researchers compare different ways of capturing skills—at the task level versus subtask level, and as text versus code—and find that breaking skills into subtasks and expressing them as text leads to better transfer across new tasks.

Bo Liu, Simon Yu, Yiding Jiang et al.

Making environment design itself learnable—rather than hand-curated or static—enables continuous self-improvement in language agents by automatically generating appropriately-difficult, diverse training tasks.

SPADE is a self-play framework where a single language model learns two roles: designing custom training environments as executable code, and solving problems within them. The environment designer learns to create challenges at the edge of the agent's abilities, automatically adapting as the agent improves.

Jayjun Lee, Jessica Yin, Asif Rana et al.

Pre-training on a generic manipulation task and using a stable fine-tuning recipe with behavior cloning and conservative updates enables efficient transfer of dexterous skills to new tasks and real robots without catastrophic forgetting.

ADEPT is a reinforcement learning framework that trains dexterous robot hands to perform complex manipulation tasks by first learning a general object-handling skill, then adapting it to specific downstream tasks. The system transfers from simulation to real robots with multi-fingered hands (23-29 degrees of freedom) using vision and touch sensors, solving long-horizon tasks at human-level speed.

Zhu Zhang, Jixun Wang, Xiaoang Xu et al.

When training smaller models on long-context tasks, combining token-level teacher guidance with task-level verifier rewards—by measuring and distributing their disagreement—works better than either signal alone.

This paper addresses a key problem in training smaller language models for long-context tasks: teacher models give token-level guidance that looks locally correct but misses global evidence or violates task constraints.

Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang et al.

Combining contrastive and triplet losses with semi-hard negatives on a lightweight encoder (MobileNetV3) effectively learns to match vocal imitations to sound effects, balancing accuracy with deployment efficiency.

This paper describes winning approaches for a challenge on finding sound effects by imitating them with your voice. The team tested two methods: one using a frozen pre-trained audio encoder with contrastive learning, and another using a mobile-friendly encoder trained with both contrastive and triplet losses. Both approaches help match vocal imitations to actual sound recordings.

Sotirios P. Chatzis, Loukas Papadoulas

Lévy Attention replaces softmax with a probabilistic formulation that automatically outputs calibrated uncertainty estimates alongside predictions—no extra parameters or passes needed, making it practical for high-stakes applications like patient risk ranking.

This paper introduces Lévy Attention, a new attention mechanism for time series that predicts both values and uncertainty in a single pass. Instead of using softmax, it formulates attention as a stochastic integral over a Poisson random measure, which naturally captures prediction confidence through two signals: disagreement (value spread) and evidence (compatibility mass).

Zachary Speck, Asa Shepard

Individual training examples can be learned and then completely forgotten during pre-training, leaving virtually no measurable impact on final model behavior or internal structure, suggesting that what matters for final performance is the aggregate signal, not individual data points.

Researchers trained 32 GPT-2 models from scratch and injected a single training example at peak learning rate to measure its actual impact. The example was learned immediately but completely forgotten by the end of training, leaving no detectable trace in the final model's weights, geometry, or performance—despite moving the model within its loss basin during training.

Thales Bertaglia, Catalina Goanta, Gerasimos Spanakis et al.

This benchmark helps detect undisclosed advertising in child-facing content by combining video transcripts, metadata, and linked sales pages—revealing widespread non-compliance with YouTube's disclosure requirements.

ChildSafeAds is a shared task that identifies commercial content in YouTube videos targeting children. Using 3,360 videos with sponsor segments from SponsorBlock, systems classify what products are promoted, categorize them, and flag legal risks. The dataset reveals that 45.5% of videos fail to properly disclose paid promotions, highlighting gaps in platform compliance.

Anran Wang, Wen Shi, Yong Luo et al.

AI climate predictions become more trustworthy when you can explain which physical mechanisms drive the forecast; here, interpretability methods confirmed the model's drought prediction is based on real atmospheric physics, not spurious patterns.

Researchers used a deep learning model combined with interpretability techniques to predict a summer 2026 drought in central China.

Ramneet Kaur, Pradyumna Chari, Ramesh Raskar et al.

AI agents communicating through hidden internal states can coordinate deception undetectably—but you can monitor and prevent this by tracking latent activations and using counterfactual analysis to steer behavior back to compliance.

This paper addresses a critical safety problem: AI agents can coordinate harmful behavior through hidden communication channels in their internal states, invisible to human oversight.

Tomasz R. Bielecki, Thibaut Mastrolia, Haoze Yan

You can apply continuous-time reinforcement learning to complex, memory-dependent systems by first converting them to Markovian approximations—this opens up RL applications to event-driven processes like financial markets and network dynamics.

This paper tackles reinforcement learning for systems driven by Hawkes processes—mathematical models where events cluster in time and influence future events. The key challenge is that these systems have memory and aren't Markovian, so standard RL doesn't apply.

Tate Berenbaum, Muthaiah Venkatachalam

Pipeline parallelism with pre-compiled shards lets you run 70B models on clusters of consumer AI PCs at interactive speeds by splitting computation across machines and optimizing GPU utilization through careful kernel fusion and speculative decoding.

This paper shows how to run large language models (like Llama 70B) across multiple Intel AI PCs by splitting the model into layers and running each layer on a different machine. Using OpenVINO optimization, speculative decoding, and request interleaving, they achieve near-optimal speed while serving multiple users simultaneously on modest hardware.

George Andrikopoulos

Stop benchmarking AI on what it can do at its best; measure instead how consistently it does the same thing when asked the same question twice. This precision metric better predicts real-world system reliability and guides whether you need better rules or a better model.

This paper argues that AI system quality should be measured by precision (consistency of outputs across repeated requests) rather than capability (best-case performance). Using a marksman analogy, the author shows that frontier models have saturated accuracy but differ in output reliability—how tightly grouped their responses are.

Zhenyao Cui, Siyuan Kan, Siyang Li et al.

Brain-based image retrieval can work for new users without retraining by learning to recover the geometric transformation between their brain's coordinate system and a shared visual space, using only unlabeled test-time alignment.

This paper tackles cross-subject EEG-to-image retrieval—retrieving images that match brain signals from new users without labeled training data. The key insight is that different people's brains organize visual concepts similarly but along different coordinate directions.

Steven Morse, Daniel Runfola, Trenton W. Ford

Political and social topics show measurable semantic drift in online discourse over time, detectable through embedding-space analysis—a technique that could help track how language and meaning evolve around contentious issues.

This paper analyzes how topics shift and evolve in Reddit discussions over 16 years by tracking semantic embeddings of 12.7 billion comments. Using pretrained language models and unsupervised clustering, the researchers show that politically charged topics drift significantly in meaning over time, while stable domains like sports remain consistent.

Zhenyao Cui, Siyuan Kan, Dingkun Liu et al.

Brain-to-image decoding can work with far fewer repetitions by anchoring both neural and visual representations to a shared reference point, rather than just denoising the query alone.

This paper tackles brain-to-image retrieval with limited neural data. Current methods require averaging 80+ brain scans per image, but the authors show that low-repetition queries fail not just due to noise, but because brain and image representations misalign.

Bingxin Xu, Yuzhang Shang, Emilio Ferrara

Breaking long robot tasks into independently explored subtasks with explicit transition management reduces exploration cost from exponential to linear and improves success rates by 15% on benchmarks.

This paper presents BATON, a system for long-horizon robot manipulation that combines vision-language-action models with LLM planning. Instead of learning end-to-end, BATON explores individual subtasks cheaply, stores solutions in memory, and chains them together while managing transitions between subtasks using a verifier agent and handoff mechanisms.

Emilien Dupont, Marvin Eisenberger, Borislav Kozlovskii et al.

Machine learning and evolutionary optimization can solve previously intractable mathematical optimization problems, enabling incremental progress on long-standing theoretical computer science questions.

Researchers improved the best-known upper bound on the matrix multiplication exponent (a fundamental constant in computer science) from 2.371339 to 2.371177 by reformulating the underlying optimization problem, applying modern machine learning techniques, and using an evolutionary algorithm called AlphaEvolve to refine the solution.

Kejia Zhang, Youran Sun, Xinyu Ren et al.

Symbolic regression works better when you preserve the scientific reasoning behind each equation candidate—not just the formula itself—allowing AI agents to learn from failed attempts and build credible explanations for why one equation is better than others.

AutoSR is an automated system that discovers scientific equations from data by treating symbolic regression as a research investigation rather than just equation fitting.

Jiaming Li

When you have a fast but imperfect analytical model and limited expensive simulation data, teaching a neural network to correct the analytical model's errors—or pre-training it on the analytical model first—can cut your data requirements dramatically.

This paper shows how to make machine learning models more data-efficient by combining cheap analytical equations with expensive high-fidelity simulations. Using Helmholtz resonators as a test case, the authors demonstrate two approaches: learning to correct analytical predictions, or distilling analytical knowledge into a neural network before fine-tuning with limited simulation data.

Benjamin Belay

Generated text can carry cryptographically verifiable evidence about which internal computations a model actually performed, opening possibilities for auditing model reasoning without changing the visible output.

This paper demonstrates that language models can embed hidden evidence of their internal computational states into generated text. Researchers trained neural networks on arithmetic tasks with mandatory intermediate decision points, then verified which internal state was used and encoded that information as subtle statistical patterns in the output.

Shuai Huang, Zhe Qu, Zhaowei Hua et al.

Quantile-based survival prediction reveals how treatment effects vary across the survival distribution—some patients benefit early, others late—information hidden by standard hazard ratios and now achievable without crossing quantile curves.

This paper introduces a method for predicting survival times that captures how different patients respond differently at early vs. late stages of disease. Unlike traditional survival models that give a single risk number, this approach estimates the full distribution of survival times while ensuring predictions stay logically consistent (quantiles don't cross).

Girish G N, Ashutosh Sahoo, Akshay SP et al.

On-chain lending needs to separate a wallet's total token holdings from its liquid, spendable balance—two wallets with identical net worth can have very different repayment capacity depending on which assets they actually hold.

zLend is a framework that assesses borrower creditworthiness in decentralized lending by analyzing on-chain transaction history. It reconstructs daily wallet balances from token transfers in two ways—using only stablecoins and using all tokens—to distinguish between total wealth and spendable cash.

Saisab Sadhu, Aadit Sengupta, Vinay Kumar Sankarapu et al.

Current compliance monitoring systems for AI don't actually read the rules they're supposed to enforce—they work by pattern matching on scenarios, not rule logic, which undermines their use as regulatory controls.

This paper reveals that compliance detectors used to monitor language models for regulatory violations are 'rule blind'—they make the same decisions regardless of what rule they're supposed to check. The authors show that deleting or swapping rules doesn't change detection accuracy, meaning detectors rely on surface patterns rather than actual rule content.

Reza Bayat, Ali Behrouz, Vahab Mirrokni et al.

Gradually expanding memory capacity during sequence processing—rather than using static memory—is a simple, cost-free improvement that works across different memory-based architectures and shows larger gains on longer contexts.

This paper introduces Proteus, a technique that improves how neural networks handle long text by gradually expanding memory capacity as more context arrives.

Langzhe Gu, Chengkai Hou, Meng Li et al.

By decomposing humanoid control into hierarchical stages and constraining RL to a learned latent space, you can adapt large VLA models to real robots efficiently without retraining the backbone or excessive real-world exploration.

This paper presents HAF, a framework for adapting general-purpose vision-language-action models to humanoid robots performing complex whole-body tasks. The approach uses hierarchical action generation to coordinate locomotion, posture, and arm manipulation, then refines the policy through efficient reinforcement learning in a compact latent space without updating the large foundation model.

Enric Boix-Adsera, Benedict Tessler

AI models can be reliably manipulated through combinations of weak, inconspicuous textual cues that individually appear harmless but collectively override intended behavior—a vulnerability that's hard to detect and poses significant safety and interpretability challenges.

Researchers show that AI models can be controlled through subtle, seemingly irrelevant text cues combined together—a phenomenon called 'model hypnosis.' These inconspicuous prompts work across different models and scales, including advanced reasoning models, and can transfer between systems.

Samarasimha Reddy Chittamuru, Ayhan Akinturk, Allison Kennedy et al.

When validating ML models on time-series data like operational sensor readings, use time-aware cross-validation instead of random splits—random splits create unrealistic performance estimates that won't hold in real deployment.

This paper reveals a critical flaw in how machine learning models for ship fuel consumption are validated: most studies use random train-test splits that leak temporal information and give overly optimistic results.

Minh-Ha Nguyen, Cathy Shyr

You can improve a frozen language model's performance on specialized tasks by treating policy refinement as a human-in-the-loop process: have an AI critic identify recurring failures, propose natural-language policy changes, and let domain experts decide what gets deployed.

This paper presents Policy Iteration with Human Feedback (PIHF), a method that improves a fixed language model's performance on rare-disease diagnosis by iteratively refining its decision-making policy through human expert review.

Jonathan Sadeghi, Jenny Seidenschwarz, Jesse Allardice et al.

Popular video generation models systematically fail to match real-world probability distributions—they collapse to a few likely outcomes rather than capturing the full range of possible physical events, which matters for any application requiring accurate uncertainty quantification.

CaliBench tests whether video world models accurately reproduce the probability distributions of physical outcomes.

Junjie Chu, Ye Leng, Mingjie Li et al.

Generative search engines are vulnerable to content optimization tactics that inflate the visibility of low-authority or false information; systematic detection methods are now possible but require careful design to avoid relying on author-based shortcuts.

This paper introduces GEO-Flag, a system for detecting web pages optimized for generative search engines (like Google's AI Overviews).

Taenyun Kim, Edyta Bogucka, Daniele Quercia

Building AI systems by aggregating public moral votes doesn't guarantee fairness; the developers' upstream design choices (feature selection, voter composition, question wording) are actually normative decisions that reshape what the public thinks they're voting for.

This paper reveals that moral AI systems trained on public votes aren't neutral—developers' hidden choices about which features to vote on, who votes, and how questions are worded significantly shape the resulting AI behavior.

Yubo Zhang, Yiyao Liu, Xiaodong Wang

Neural networks can learn efficient search strategies for MIMO detection that match or exceed traditional algorithms, and these learned policies can be transferred to soft-output receivers that improve when combined with iterative decoding.

This paper presents a learning-to-transition framework that uses Transformers and neural networks to efficiently detect high-order MIMO signals. The approach treats detection as a sequence of symbol transitions, learns to search the discrete symbol space effectively, and integrates with channel decoding through an iterative receiver that adapts based on decoder feedback.

Zhelun Wu

When combining evidence from multiple sources, separate the task of interpreting each source from aggregating those interpretations—use structured tuples and calibrated scoring rather than simple concatenation and vote counting.

This paper separates evidence interpretation from decision aggregation in multi-source reasoning systems. Instead of concatenating sources into one prompt, the authors propose a structured evidence tuple (hypothesis, reliability, rationale, provenance) and show how to properly combine interpretations using calibrated log-likelihood ratios.

Pin-Yen Huang, Sachin Chhabra, Prasanth Sai Gouripeddi et al.

Hierarchical structure matters: representing recipes as nested sequences of structured steps, rather than flattened tables, lets models learn procedural dependencies and field interactions that improve performance on real-world synthesis and manufacturing tasks.

RecipeNet is a hierarchical Transformer model designed to learn from recipe data—ordered sequences of steps with structured fields—used in materials science, pharmaceuticals, and manufacturing. Unlike traditional tabular methods that flatten this data, RecipeNet captures both field interactions within steps and dependencies across steps, achieving better performance on recipe-based tasks.

Juno Nam, Bowen Deng, Xiaochen Du et al.

TIP makes free energy calculations as fast as traditional potential energy calculations, enabling researchers to predict material phase stability at any temperature and pressure from a single model evaluation—critical for discovering new materials.

This paper introduces thermodynamic interatomic potentials (TIP), machine learning models that predict free energies and phase stability of crystalline materials across temperatures and pressures.

Hanfeng Lu, Tianyu Feng, Suyi Li et al.

By overlapping independent computation phases and sharing GPU memory intelligently, you can train vision-language models 1.2–2.2× faster without needing more hardware or changing your RL algorithm.

Rollplex is a GPU runtime that speeds up vision-language model training by overlapping different computational phases.

Hao Yan, Lisa Pilgram, Dan Liu et al.

You can now generate realistic synthetic tabular health data from multiple heterogeneous sources by standardizing them statistically first, then using diffusion transformers to learn and reproduce their patterns.

This paper presents a method for generating synthetic health data from multiple different database tables with varying structures. It works in two stages: first converting diverse tables into a standardized statistical format, then using a diffusion transformer to learn patterns and generate new synthetic tables.

Ben Anson, Conor Houghton, Edward Milsom

Muon can now be used effectively with LoRA-style low-rank fine-tuning by approximating its solution mathematically, offering a practical way to combine two previously incompatible techniques.

This paper solves a mathematical incompatibility between Muon (an optimizer that works well for pretraining) and LoRA (a popular parameter-efficient fine-tuning method). The authors propose sMuon, which approximates Muon's benefits in the low-rank setting through linearization and least-squares, using only standard matrix operations.

Charitha Nandepu, Lohitha Kalepu, Gabriele Ciavarella et al.

Data-driven surrogate models can replace expensive traffic simulations in maintenance planning, making it practical to optimize when and where to schedule road work based on traffic impact.

This paper develops fast machine learning models to predict traffic flow patterns when roads are under maintenance. Instead of repeatedly solving complex traffic equations (which is slow), the authors train surrogate models on real traffic data to quickly estimate how road closures affect traffic. They test this on Newark, NJ traffic data as a foundation for automated maintenance scheduling.

Alexy Skoutnev, Kirill Acharya, Gaston Longhitano et al.

Test-time world model inference—where an AI builds and validates its own simulation of unknown environments—dramatically improves task performance (from 7.8% to 93.3%) and shows that learning game rules is easier than inferring goals.

Twin is a system where an AI agent builds executable world models to solve puzzle games by observing and simulating gameplay. Rather than hand-coding rules for each game, the system learns the game's mechanics through trial and error, validates predictions against actual outcomes, and fixes mismatches. It solves 97.8% of test games more efficiently than humans.

Abhishek Shukla, Ankur Sinha, Faiz Hamid

By formulating architecture search as a linear program using gradient and curvature information, LP-NAS finds better neural network designs 2-3x faster than standard differentiable NAS methods while achieving higher accuracy.

This paper proposes LP-NAS, a neural architecture search method that uses linear programming to find better network designs faster. Instead of randomly exploring architectures, LP-NAS uses mathematical optimization principles to guide the search, resulting in architectures that generalize better and are found more quickly than existing methods like DARTS.

Ajith Anil Meera, Pablo Lanillos, Wouter Kouw

Expected Free Energy provides a single, tunable objective that lets robots efficiently explore unknown environments by balancing information gathering with resource constraints—useful for real-world autonomous missions.

This paper applies Expected Free Energy from active inference to robotic path planning for Mars exploration. The robot must balance two goals: building accurate maps and finding high-value regions (like water sources) while staying within travel and measurement budgets. The approach outperforms traditional information-seeking methods by unifying both objectives under one principled framework.

Ziyang Luo, Zhongyao Chu, Xinjie He et al.

You can improve a frozen language model's reasoning and confidence detection in one forward pass by reconstructing clean internal states after steering, rather than running separate passes or accepting interference between techniques.

This paper solves a key problem in frozen language models: they both misuse their internal knowledge and fail to recognize when they lack sufficient information to answer.

Yaxin Luo, Haobin Jiang, Jialv Zou et al.

You can automatically optimize how agents interact with tools by having them learn from rollout feedback—AutoDesign shows this approach consistently improves performance across different models and scales to real-world tasks.

AutoDesign is a framework that automatically improves how AI agents interact with tools by learning from feedback. It treats complex tasks like converting academic papers into posters as a long-horizon process where a meta-optimizer guides a code agent to refine its interaction patterns.

Bobo Li, Hao Fei, Tianjie Ju et al.

Direct perception of raw scientific data—not just text summaries—is critical for AI systems to conduct rigorous, evidence-grounded research. OmniScientist shows that multimodal input improves all aspects of automated scientific discovery.

OmniScientist is an AI system that conducts scientific research across multiple disciplines by directly processing raw data in many formats—images, videos, audio, 3D structures, tables, and more.

Dairu Liu, Zekun Qi, Jiayu Zeng et al.

Standard motion tracking metrics miss the contact and stability problems that matter most to humans—HumanScore, trained on human preferences, better predicts what actually looks right in humanoid movement.

HumanTracker is a new benchmark for evaluating humanoid motion tracking that focuses on what humans actually perceive rather than just pose accuracy. It includes 153 hours of professional motion capture data and introduces HumanScore, a metric trained on human preferences that catches physical problems like foot skating and unstable balance that traditional metrics miss.

Shangao Li, Yao Zhang, Volker Tresp et al.

Don't trust matched evaluation scores for coding agents—they hide failures introduced by command serialization and parsing.

This paper reveals that standard evaluation metrics for LLM coding agents can hide critical failures in command execution. By testing how Bash commands survive serialization and parsing in different system configurations, the authors show that matched scores mask up to 73% of actual failures—failures introduced not by the model but by how its output is processed.

Fanfei Li, Jana Zeller, Manuel Prada-Corral et al.

Training on a carefully curated, grade-level-appropriate curriculum creates a sandbox for studying knowledge acquisition with clear boundaries—useful for understanding how models learn and what happens when you try to teach them new concepts.

Researchers created LittleLeaner, a 5B-parameter language model trained on an 88B-token curriculum limited to U.S. Grade 5 material, to study how models acquire knowledge under controlled conditions. Unlike models trained on messy web data, LittleLeaner has clear, interpretable knowledge boundaries, making it easier to understand what the model knows and how it learns new information.

Weihan Meng, Hongzhu Guo, Yi Jing et al.

You can teach an LLM to explain what individual features in another LLM do by injecting those features and training it to verbalize their meaning—this works across different SAE dictionaries and even different models.

SAEVerbalizer trains language models to generate natural-language explanations for features extracted by sparse autoencoders (SAEs). Instead of observing model behavior to understand what features do, the system directly injects feature directions into the model and fine-tunes it to produce explanations, making the process faster and more scalable.

Tianyi Li, Yaxin Luo, Xinyi Shang et al.

DARTree achieves up to 9.73× speedup on language model inference by constructing and scoring candidate token trees in parallel, accepting nearly 13 tokens per verification step—nearly 3× more than prior methods.

DARTree speeds up language model inference by using a tree-based speculative decoding approach. Instead of generating one draft token at a time, it predicts multiple token candidates in parallel using diffusion, then verifies them together. The method uses an autoregressive correction head to score entire branches at once, accepting more tokens per verification round than existing methods.

Zhe Ye, Hantao Lou, Yuechun Sun et al.

AI agents can generate code, but generating code with formal proofs that work together across entire repositories remains an unsolved problem—current best agents only solve 27 of 43 real-world instances.

Vero is a benchmark for evaluating whether AI agents can generate both correct code implementations and machine-checked formal proofs together across real multi-module software repositories.

Martin J. Wainwright

UGC provides a principled way to design adaptive masking schedules for discrete diffusion that are provably near-optimal, with potential √d speedups over fixed schedules by concentrating computational effort where data geometry demands it.

This paper introduces unmasking growth complexity (UGC), a geometric measure that controls how discrete diffusion models should reveal information during sampling. The key insight is that UGC increments directly bound discretization error, enabling optimized sampling schedules that adapt to data structure.

Yunsung Chung, Yingshuo Liu, Abboud F. Hassan et al.

Clinical prediction improves when models treat recovery as an evolving process rather than a static snapshot—incorporating asynchronous post-procedure events and imaging can significantly boost outcome forecasting accuracy for cardiac interventions.

This paper presents a clinical AI model that predicts post-surgery outcomes in heart rhythm procedures by tracking how a patient's condition evolves over time.

Peter Schneider-Kamp, Jacob Nielsen, Gianluca Barmina et al.

You can build competitive frontier-level language models at 1B parameters using only openly licensed data, making it feasible for researchers and organizations to develop ethical AI without relying on scraped or restricted datasets.

Mimir v1 is a 1-billion-parameter language model trained entirely on permissible (legally and ethically sourced) data that achieves competitive performance with much larger models. It uses a Hierarchical Reasoning Model architecture and excels at English, math, code, and Danish tasks—showing that high-quality open-source models don't require massive proprietary datasets.

Yuto Nishida, Hirokazu Kiyomaru, Yusuke Oda et al.

You can measure training data importance during pretraining by tracking parameter trajectories, revealing that different data types matter at different training stages—literature early, STEM later—without needing task-specific validation sets.

This paper introduces a new method to measure how much training data influences language model development without needing to pick specific downstream tasks. Instead of testing on particular benchmarks, the researchers measure influence by tracking how each piece of training data pushes the model toward its final parameters.

David Chushig-Muzo, María Ángeles Rodríguez de Cara, Eva Milara et al.

Self-organizing maps can encode tabular data more effectively than simpler dimensionality reduction by capturing feature relationships alongside values, improving both predictive performance and model interpretability.

TabSOM converts tabular data into images using self-organizing maps, preserving both feature values and relationships between features. Unlike existing methods that only encode individual feature values, TabSOM captures feature interactions as spatial patterns, enabling vision models to achieve better performance while remaining interpretable.

Nestor R. Barraza, Gabriel Pena

Machine learning performance has hard mathematical limits set by data structure, not just algorithm choice. Understanding these limits requires proper models of how data is generated, especially for systems with feedback like LLM agents.

This paper examines fundamental limits on what machine learning systems can achieve, showing that performance is constrained by the structure of the data itself rather than just algorithm design.

Lei Bai, Jiaqi Cao, Chiyu Chen et al.

This work demonstrates how to build AI agents for science by combining multimodal pre-training with agentic reinforcement learning and memory-augmented architectures, achieving strong performance on scientific reasoning and forecasting tasks without requiring task-specific model modifications.

Intern-S2-Preview is a large multimodal AI system designed to tackle scientific discovery tasks by reasoning over diverse data types, using scientific tools, and working across long-horizon problems.

Sabin Roman, Ljupco Todorovski, Saso Dzeroski

SORT shifts equation discovery from brittle library selection to basis design: by learning sparse coefficients in well-chosen orthogonal bases, it provides a more stable intermediate representation that gracefully degrades under noise and sampling sparsity.

SORT is a machine learning technique that learns compact mathematical representations of dynamical systems from noisy, irregularly sampled data by fitting sparse coefficients in orthogonal basis expansions.

Yi-Chung Chen, Philip Jacobson, Tom Lampo et al.

Using physical trajectory data as training supervision helps video embedding models better understand motion-centric driving events, improving retrieval accuracy by 5-10% while keeping inference simple and efficient.

This paper tackles retrieving relevant driving video clips from large datasets by improving multimodal embedding models. The key innovation is TraVEL, which fine-tunes video embeddings using trajectory (vehicle motion) as training supervision, helping the model understand motion-centric events like turning or accelerating.

Dananjay Srinivas, Saksham Khatwani, Maria Pacheco

LLMs possess the internal machinery to recognize knowledge gaps and adjust specificity accordingly, but their generation process doesn't use these signals—a gap that could be fixed through better training objectives.

Large language models often make up specific details about unfamiliar entities instead of admitting uncertainty. This paper shows that LLMs actually have internal signals detecting when they don't know something and can anticipate how specific their answer should be—but they ignore these signals during generation, preferring to sound confident anyway.

Julian Minder, Viktor Moskvoretskii, Raghav Singhal et al.

Installing alignment values during pretraining from the beginning creates deeper, more robust alignment than adding it after training, and this advantage grows with more pretraining data.

This paper introduces Synthetic Persona Pretraining (SPP), a method that embeds desired values and assistant behavior directly into language models from the start of pretraining rather than adding them afterward.

Saisha Shetty, Satvik Tripathi, Austin Lin et al.

For clinical AI, breaking monolithic prompts into traceable multi-agent workflows improves interpretability and failure diagnosis compared to single-LLM approaches, while remaining accessible to non-programmers.

MARC is an open-source framework that replaces single-prompt LLM calls with coordinated teams of specialized agents for clinical reasoning. Each agent handles a specific task (extracting data, reasoning, generating answers, evaluating results), with clear handoffs between stages so failures can be traced.

Mohammed Ayman Habib, Rylan Hart, Morteza Fayazi

LLMs can automate the full analog circuit design process—not just sizing or topology separately—by using multiple agents that debate and refine designs, achieving 40x faster results than manual approaches while discovering novel circuit topologies.

AaLLM is an end-to-end framework that uses multiple AI agents working together to automatically design analog circuits from scratch. It takes design requirements as input and outputs complete circuit designs (both the circuit structure and component values), reducing the time-consuming manual work that circuit engineers typically do.

Yuchen Xin, Zhihua Zhang

For sampling from nonsmooth targets, tracking local curvature via the active trace gives tighter convergence guarantees than worst-case dimension-dependent bounds, achieving O(ε⁻²) instead of O(ε⁻³) for common structured problems.

This paper analyzes the Moreau-Yosida unadjusted Langevin algorithm (MYULA) for sampling from nonsmooth distributions. The key contribution is showing that the algorithm's convergence depends on an 'active trace' quantity (measuring local curvature) rather than global dimension, leading to improved complexity bounds—especially for structured penalties like lasso and total variation.

Daniel Perkins, John Squires, Janou Milligan et al.

Instead of training separate models for each domain, you can use an MLLM router to dynamically select which vision backbone handles each image, getting both better generalization and easier updates without retraining.

ARMDIL uses a multimodal language model to intelligently route images to the best-suited vision model (CNNs, self-supervised learners, or vision-language models) within an ensemble.

Jim Woodcock, Gabriel Leite, Augusto Sampaio et al.

When using LLMs to modify formal proofs, you need independent verification beyond just checking if the code compiles—CAPRI shows that contract-based auditing can catch unauthorized changes that Isabelle alone would miss.

CAPRI is a system that uses LLMs to help repair broken Isabelle proofs while ensuring developers maintain control over what gets changed. It combines Isabelle's proof checker with an independent contract enforcer that tracks all changes, keeping an audit trail of prompts, proposals, and verdicts.

Van Khoa Nguyen, Alexandros Kalousis

By reversing the physics concept of spontaneous symmetry breaking, this diffusion model generates complete crystal structures with proper global symmetries—a significant improvement over methods that only generate partial specifications.

This paper proposes a new method for generating crystal structures by using a diffusion-based model that starts from low-symmetry configurations and gradually breaks symmetries to create complete crystal specifications.

Avinash Kori, Fabrizio Russo

World models need to learn causal structure and entity interactions, not just predict observations. This enables agents to reason about why things happen and make better decisions outside their training distribution.

This paper proposes a framework for understanding world models through a causal lens, arguing that effective world models should capture not just how to generate future observations, but also the underlying causal structure—entity properties, interactions, and dynamics—that explains environmental behavior.

Yukun Dai, Mingzhe Dai, Tianshi Wang et al.

VLA robotic models share cross-task vulnerabilities that can be exploited with a single adversarial texture, revealing a critical safety gap in multitask embodied AI systems that current defenses don't address.

This paper demonstrates how a single adversarial texture on a 3D object can fool vision-language-action (VLA) robotic models across multiple tasks simultaneously. Rather than crafting separate attacks for each task, the researchers optimize one texture that works universally by backpropagating gradients through a differentiable renderer, reducing task success rates from 90% to 48% in experiments.

Md Wasiul Haque, Sagar Dasgupta, Mizanur Rahman et al.

LLM-generated test harnesses for safety-critical autonomous vehicle code fail primarily due to build system complexity, not reasoning ability—suggesting that tooling and integration matter more than model capability for dynamic vulnerability analysis.

This paper investigates whether LLMs can automatically generate executable test artifacts to confirm exploitable weaknesses in Autoware, an autonomous vehicle software stack.

Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi

By predicting contact-rich manipulation failures in latent visual space before they happen, you can abort unsafe actions without retraining the underlying policy—enabling safer robot manipulation with minimal overhead.

ContactGuard is a safety system that predicts manipulation failures before a robot makes contact with objects. It uses a learned world model to simulate the visual consequences of planned actions in a compact latent space, then checks if the predicted outcome looks like failure.

Andy Yang, Blerta Veseli, Corentin Barloy et al.

Transformers' ability to generalize to longer sequences depends on specific algebraic properties of the language being learned—properties that classical finite algebra misses but can be captured by extending decomposition theory to infinite groups.

This paper characterizes which regular languages transformers can generalize to longer sequences than they've seen during training. The authors develop new algebraic theory extending classical decomposition methods to handle transformers' unbounded counting abilities, providing a polynomial-time algorithm to predict length generalization on any regular language.

Irina Proskurina, Mayank Kumar, Oyindolapo O. Komolafe

Instruction tuning makes models sound more confident without improving accuracy, and it reduces the diversity of explanations they provide—a potential concern for transparency and reliability.

This paper investigates how instruction tuning affects language models' confidence levels and the diversity of their explanations. The researchers found that instruction tuning makes models express higher confidence in their answers, but this doesn't match improvements in actual accuracy.

Juan Irving Vasquez, Juan Terven, Laura-Ivoone Garay-Jimenez

Instead of using a single LLM to judge AI readiness, using multiple specialized LLMs with a conservative aggregation rule produces more reliable and honest assessments of project maturity.

This paper creates AIRL, a unified 9-level AI readiness scale combining three existing frameworks, and RAIL, an automated classifier using multiple specialized LLMs to assess AI project maturity. The system evaluates projects across six dimensions (data, specifications, expertise, algorithms) and prevents overestimation through a conservative review process.

Zixuan Lan, Yanhong Li, Jiawei Zhou

You can speed up LLM inference by skipping less important values in matrix operations—attention layers benefit most from this, while MLPs are harder to compress—and the speedups scale better on longer inputs.

This paper introduces Reduced Matrix Multiplication (RMM), a training-free method that speeds up language model inference by selectively removing less important numerical values from matrix multiplications. The approach works without changing model weights and maintains accuracy while reducing computation, with benefits increasing at longer sequence lengths.

Serli Kopar, Sam Gijsen, Abner Hernandez et al.

Speech-based disease detection models may not be learning genuine disease characteristics but rather dataset-specific patterns, raising serious concerns about their reliability for real-world clinical use.

This paper investigates whether speech models trained to detect Parkinson's disease actually learn disease-specific patterns or just exploit dataset quirks.

Aimilios Hadjiliasi, Louis Nisiotis

Small language models can efficiently run agent cognition (thinking and memory) on edge devices like Jetson boards, enabling virtual agents to operate independently in real-time without cloud latency.

This paper explores how small language models (SLMs) running on edge devices can power the cognitive processes of virtual agents in immersive worlds.

Yikai Xu, Zhao Chen, Jian Huang

You can robustly learn from contaminated data by selecting the subset of samples that maximizes Wasserstein distance from the full dataset—this works as a model-agnostic preprocessing tool before training any model.

This paper introduces Wasserstein Filtering, a method to clean contaminated datasets by selecting samples whose distribution is most different from the full dataset. The approach uses optimal transport theory to identify and remove outliers, with theoretical guarantees and practical algorithms that work as a preprocessing step for any downstream task.

Yiwei Li, Wanli Yang, Hexiang Tan et al.

Current AI agents excel at combining existing techniques but lack the consistency and genuine innovation needed for autonomous research; improving their performance requires better experience management and more stable inference strategies, not just larger models.

This paper evaluates how well AI agents can autonomously improve technical systems through long-horizon experimentation. Rather than just looking at final scores, the researchers developed a framework to analyze what happens during the process—how agents formulate solutions, execute them, and learn from feedback.

Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut et al.

When robots have limited time to practice, you can use optimization to compute exactly which skills to learn and how long to spend on each—trading off learning difficulty against the reward value each skill unlocks.

This paper tackles how robots can learn skills efficiently when practice time is limited. The authors propose Deliberate Practice, an algorithm that decides which skills to practice and for how long to maximize task performance within a fixed budget. They use a mathematical optimization approach to find the best allocation of practice time across different skills.

Mirko Tritella, Riccardo Pozzi, Matteo Palmonari

When building RAG systems over sensitive texts like parliamentary records, weighting sources by topic-dependent expertise rather than frequency prevents political bias and ensures accurate attribution.

ParliamentRAG is a retrieval system for Italian parliamentary transcripts that addresses bias in AI-generated summaries by modeling speaker authority based on expertise, profession, and topic relevance.

Paul Savala

Predicting course enrollment alongside grades significantly improves academic performance forecasting—jointly modeling what students take and how they'll perform is more accurate than predicting grades alone.

This paper presents TRACE, a transformer-based model that jointly predicts which courses students will take and their grades in those courses for upcoming semesters. Unlike traditional approaches that treat student history as a simple sequence, TRACE captures how courses taken concurrently within a semester affect performance, reducing prediction error by nearly 50% compared to grade-only models.