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Research

New AI papers from arXiv, picked daily and explained with their claims, benchmark results and code.

2566 articles · page 9 of 54 · The Research front page

AllAgentsLanguage modelsReinforcement learningVisionGenerative mediaSpeech & audioRobotics & embodied AISafety & securityEvaluation & benchmarksEfficiency & systemsCode & softwareAI for science & healthTheory
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Agents

Foundational LLM capabilities only partially predict agentic execution gaps

The authors develop a framework that links foundational LLM benchmarks to agentic operations by measuring capability realization gaps, deviations of observed execution behavior from predictions based on offline capability scores. Testing 15 model services on the τ²-Bench Banking tasks, they find that foundational profiles explain only part of the variation in operation scores; models with similar benchmarks can differ substantially in execution, and these gaps persist across task subsets. Adding operation profiles substantially improves held-out prediction of overall task reward.

Generative media

Single-stage RL method unifies preference alignment and few-step image generation

The authors propose AdvFidelity, which integrates artifact-sensitive fidelity supervision into reinforcement learning (RL) through an online adversarial reward. This enables joint reward alignment and high-fidelity few-step generation within a single RL stage, without explicit distillation objectives. Across two model backbones and three task settings, AdvFidelity matches full-step LeapAlign in alignment, task performance, and overall generation quality using only 4 or 8 sampling steps without classifier-free guidance.

Language models

Continuing long-context pre-training keeps improving models, and gains are predictable

Ziqing Qiao, Xiaoyue Xu, and colleagues at Tsinghua University and OpenBMB ask whether long-context continual pre-training (LCCP) yields sustained, predictable improvements as its token budget grows. They fit an additive power law to 109 training configurations across five model scales, introduce a stable checkpoint-averaging protocol for monitoring open-ended LCCP, and show that benefits persist through context extension and supervised fine-tuning without clear saturation.

Language models

Masked diffusion language models improve by encoding position priority as hierarchy levels

Gupta et al. introduce Hierarchical Masked Diffusion Language Models (HMDLM), which extend masked diffusion with a hierarchy variable per position and derive the corresponding forward/reverse processes and NELBO objective. In two-stage decoding, the model first predicts which positions are high-priority, then fills tokens conditioned on that assignment. The approach reduces perplexity by up to 7 points on WikiHow and improves Sudoku solution accuracy by up to 8.6 absolute points over diffusion baselines.

Efficiency & systems

Reusing target probabilities in speculative decoding can improve tree selection but requires careful trade-offs.

The authors investigate when next-token probabilities from a target model's verification pass can be reused to select the next tree of draft tokens. They find that while exact reuse is only guaranteed under restrictive conditions that cost accepted tokens, using a separately computed probability distribution for the first token (root row) improves acceptance rates and decoding speed. In experiments across six language models, both root selection and root-draw selection outperform draft-based ranking.

Safety & security

Edge vision-language models are as physically attackable as classic object detectors

The authors benchmark eleven detection and grounding models against physical adversarial patches worn on a person, introducing a conditional attack success rate that only scores images where a model still localizes under a matched gray occluder. They find the compact VLMs are as vulnerable as classic detectors. Failure modes split into omission, substitution, and displacement, with the latter two producing confident, well-formed boxes that downstream planners cannot distinguish from correct detections.

Language models

Shaping teacher behavior improves self-distillation for language models

The authors propose TeachBack, a method for on-policy self-distillation that shapes the teacher's generation behavior by controlling the length of reference reasoning and prompt style. Using a bidirectional preference objective, the teacher guides the student while the student's behavior feeds back to improve future teacher supervision. Experiments on mathematical and logical reasoning tasks show that TeachBack consistently outperforms existing OPSD methods.

Vision

Balancing within-person and between-person variation improves clothes-changing re-identification

Researchers from Reichman University and Microsoft introduce MINER, a model-agnostic training framework that explicitly balances intra- and inter-person variance for Clothes-Changing Person Re-Identification (CC-ReID). By stochastically mining hard positive and hard negative examples and adaptively controlling the variance trade-off, MINER improves retrieval across four CC-ReID benchmarks and ranks among the top three in all three tracks of the TVRID 2026 challenge.

Evaluation & benchmarks

Simulated users test coding agents as requirements unfold across conversations

Brehme and colleagues introduce SWE-DIVE, a framework for evaluating coding agents through conversations rather than a single complete task description. In a preliminary evaluation across 11 tasks, the authors report that simulated user personas produce distinct interaction behaviors and that unconstrained or changing requirements lead to more code regressions, changes that break previously working behavior.

Robotics & embodied AI

World models calibrated to hidden physics via short interaction clips

The authors propose PhysiCalWorld, a method that calibrates action-conditioned world models to instance-specific dynamics by prompting them with a short exploratory interaction clip. The clip is compressed into a compact physics descriptor token that grounds subsequent predictions in the actual physical behavior of the scene. Without requiring physics labels, the method substantially improves rollout prediction accuracy and visual planning success across diverse environments.

Evaluation & benchmarks

Benchmark tests proactive memory for wearable AI assistants in streaming egocentric video

The authors introduce ProactEgoMem, a benchmark for proactive memory in streaming egocentric video. It defines three object-centric tasks, Information Capture, State Tracking, and Location Tracking, at two levels of proactiveness (notification and intervention). Evaluating multiple proprietary, open-source, video-language, and streaming baselines shows that even the strongest models fail to capture memory both timely and accurately.

Generative media

Time-resolved diagnostic separates irreducible velocity ambiguity from reducible training error

Mei et al. propose the Velocity Ambiguity Profile (VAP), a time-local Bayes-risk floor for velocity prediction in flow matching, and prove that the time-resolved loss decomposes as L_N(t) = A_v(t) + R(t;N), where only R(t;N) can be reduced by more data or a better estimator. In controlled Gaussian-mixture experiments the diagnostic behaves as predicted, and on CIFAR-100 DINOv2 feature flow guided sampling cuts uniformly evaluated velocity loss by 6.78% and global feature Fréchet distance by 23.6%, though class-conditional KID shows no improvement.

Generative media

Future chunk prediction improves physical plausibility in autoregressive video generation

The authors introduce Future Chunk Prediction (FCP), an auxiliary objective for training autoregressive video diffusion models. In addition to standard teacher-forced denoising, FCP predicts a future chunk without access to intervening visual context. They report improved physical plausibility across model scales and conditioning settings, with a lower overall training compute budget, suggesting that future-chunk supervision efficiently models visual evolution.

Evaluation & benchmarks

Tree-based method reveals fine-grained LLM capabilities hidden by aggregate scores

The authors propose TIDE, a method that induces a decision tree from benchmark samples and model scores to diagnose fine-grained capabilities. Applying TIDE to five models across four benchmarks, they find that aggregate scores conceal substantial variation: for example, Claude Opus 4.6 is perfect on microeconomic reasoning while GPT-5.4 scores 83.3%, but GPT-5.4 outperforms on contract law reasoning (88.9% vs 77.8%).

Agents

Projecting conflicting gradients during joint RL and distillation improves agent training

The authors introduce CORD, a conflict-aware gradient combination framework for joint reinforcement learning and self-distillation. It detects when RL and distillation gradients oppose each other and projects the non-protected gradient onto the orthogonal complement of the protected one before combining. Across three joint-training recipes, CORD improves ALFWorld success by 1.5 to 15.9 percentage points, and one WebShop recipe gains 3.9 points.

Evaluation & benchmarks

Benchmark tests LLM agents on recreating published computer science contributions

The authors introduce FrontierCS 2, a benchmark of 48 open-ended tasks across 13 computer science domains. An agent inherits a repository with a paper's contribution deleted and must submit patches to beat the original code on every metric and workload, judged privately. The best agent, Claude Opus 5.5, passed 58% of runs, but no agent passed any of 16 tasks, and most high-scoring runs still failed on some metric.

Evaluation & benchmarks

Coding agents struggle with system-level performance optimization in microservice systems

Zhang et al. introduce MicroPerf-Bench, a benchmark for evaluating coding agents on end-to-end performance optimization in microservice systems. They injected empirically grounded performance anti-patterns into seven real-world open-source systems, creating 65 optimization instances. Evaluating nine agent-model configurations over 1,755 runs, the best configuration achieved a normalized score of only 42.0%, with the dominant failure being functionally correct changes that failed to improve performance.

Theory

Differentially private online prediction achieves regret proportional to square root of best expert's loss

Bo Li and Peng Ye present the first differentially private algorithm for online prediction from experts that attains regret Õ(√L*), where L* is the cumulative loss of the best expert. This improves over the standard Õ(√T) regret when L* is much smaller than T, showing that privacy incurs only a small additional cost in the low-loss regime. The algorithm works under an oblivious adversary and its regret matches the non-private lower bound up to poly-logarithmic factors for constant privacy parameters.

Theory

Computing approximate correlated equilibria is as hard as computing Nash equilibria.

The authors show that computing an approximate correlated equilibrium of a polytope game is PPAD-complete, making it polynomial-time equivalent to finding a Nash equilibrium. They introduce fixed sets, a relaxation of fixed points, and prove that computing such a set is PPAD-hard at inverse polynomial precision. This result overturns the classical view that correlated equilibria are computationally easier than Nash equilibria.

Safety & security

Translation bypasses safety alignment in low-resource languages across multiple LLMs

The authors test whether English safety alignment transfers to low-resource languages by translating harmful prompts that models refuse in English. They evaluate 10 LLMs across 78 low-resource languages from 20 language families, using a conditional metric that isolates translation bypass from existing English unsafety. Mean bypass rates range from 21.9% to 44.4%, risk concentrates in specific language families, and models do not maintain a stable ranking across conditioning schemes.

Evaluation & benchmarks

Compaction tuned for one query fails to aid later queries

The authors evaluate context compaction methods on later, unseen questions across four multi-query corpora. They find that query-aware sentence selection, which keeps sentences relevant to the current question, provides no measurable improvement over random deletion for subsequent questions. Meanwhile, word-level compressors such as LLMLingua-2 achieve significant gains on later questions by retaining more content words.