ABRA shows radiology agents excel at tool execution (89%+) but struggle with outcomes (0-25%), with oracle perception raising outcomes to 69-100%, identifying perception as the primary bottleneck.
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Containment verification proves that an agentic framework can enforce safety boundaries against any output from an unconstrained AI model by mechanized forward-simulation refinement in Dafny.
Post-2021 US housing seasonality shifted from summer to spring because residential mobility moved earlier, as documented in SIPP data and reproduced by a calibrated monthly search-and-matching model.
Distribution-Aware Reward optimizes LLM regression by treating rollouts as empirical predictive distributions and rewarding marginal improvements in CRPS quality rather than point accuracy alone.
Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
PROTEA supplies an offline interface for scoring intermediate outputs in multi-agent LLM workflows, performing backward evaluation from final answers, and iterating on targeted prompt revisions with visible score changes.
SVFSearch is the first open benchmark for short-video frame search in the Chinese gaming domain, providing a frozen retrieval environment and showing performance gaps of 13-29 points between direct QA models, practical agents, and oracle knowledge.
DISA decouples partition function estimation using offline importance sampling for distribution-matching LLM-RL, matching or exceeding online baselines like FlowRL on math and code benchmarks while retaining more strategy diversity.
Phoenix-bench shows agentic AI systems lose 37-58% resolved rate when moving from SWE-bench Verified to hardware tasks because bugs spread across parallel modules via signal flow, with testbench feedback lifting performance by 42-45% while file-level oracles add only 1.4%.
LongMemEval-V2 is a new benchmark where AgentRunbook-C reaches 72.5% accuracy on long-term agent memory tasks, beating RAG baselines at 48.5% and basic coding agents at 69.3%.
ASIA uses an LLM-based coding agent to autonomously perform system identification, tested empirically on two benchmarks while noting limitations in transparency and reproducibility.
A lightweight neural dual predictor accelerates exact LAP solvers by over 2x on synthetic data and 1.25-1.5x on real MOT and LPT tasks while preserving full optimality and scaling to N=16384.
External evolution beats internal deliberation in collective-action tasks with statistical significance but neither helps in trading, and deliberation never discovers punishment while evolution does.
LLM agents exhibit constraint decay with assertion pass rates dropping substantially as structural requirements increase in multi-file backend code generation across web frameworks.
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
Stealth Pretraining Seeding plants persistent unsafe behaviors in LLMs via diffuse poisoned web content that activates on precise triggers and evades standard evaluation.
Introduces the Indic-CodecFake dataset for Indic codec deepfakes and SATYAM, a novel hyperbolic ALM that outperforms baselines through dual-stage semantic-prosodic fusion using Bhattacharya distance.
Introduces MPT benchmark and PRefine method that models user preferences as evolving hypotheses to improve personalized tool calling accuracy with 1.24% of full-history token cost.
HealthCraft is the first public RL safety environment for emergency medicine that evaluates frontier LLMs on trajectory-level safety with a dual-layer rubric, showing low multi-step performance and high safety failure rates.
Introduces a multi-resolution spatial partitioning and scan statistic method to detect unfairness in predictive models based on movement patterns, validated as effective on synthetic datasets.
Controlled minimal-pair experiments on six repository pairs show code cleanliness leaves agent task success unchanged but cuts token use by 7-8% and file revisits by 34%.
DMN achieves over 90% attack success rate on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4 by distributing instructions, supplying multimodal evidence, and adding number chain tasks across multiple images.
A perturbation framework with Drop/Add/Flip and player-removal operations demonstrates that Bradley-Terry leaderboards are non-robust to sub-1% targeted changes that alter top ranks, Kendall tau, and confidence intervals.
ColPackAgent integrates a custom colpack Python package wrapping HOOMD-blue with MCP tools and an agent skill to enable reliable autonomous workflows for colloidal packing simulations across interactive, prompt-driven, and autoresearch modes.
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