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Training long-horizon agents to solve complex tasks requires effective supervision over extended interaction sequences. However, sparse terminal rewards obscure intermediate contributions, while on-policy distillation can lose informative teacher guidance as student-generated histories grow. To address this problem, we...
Large language models (LLMs) increasingly power autonomous coding agents such as Codex and Claude Code, yet their training corpora may contain confidential credentials exposed in public repositories or collected from private development artifacts, creating risks of memorization and subsequent leakage. Existing extracti...
Experimental results show that CataOPD outperforms current baselines, extends independent student reasoning to still-unrecovered problems, and improves out-of-distribution generalization under catalyst-free inference.
Mutation analysis as an adequacy metric for kernel-benchmark oracles is introduced: deterministic rules inject 10 compilable faults into verified CUDA implementations of 188 KernelBench problems, 7{,}384 of them with an independent kill witness; any test protocol is scored by the fraction it detects.
PowerAtlas is presented, an LLM-agent framework for electricity-computing co-scheduling that integrates historical instances, domain knowledge, and physical constraints to produce joint decisions satisfying both grid operational rules and the service-level agreements of computing tasks.
Taxonomies of the LLM hallucination phenomena and evaluation benchmarks are presented, existing approaches aiming at mitigating LLM hallucination are analyzed, and potential directions for future research are discussed.
MMDynOpt-Agent is a lightweight multimodal agent that models the dynamic optimization of multimodal reasoning as a Markov decision process via end-to-end reinforcement learning, designed to reduce the cost of multimodal reasoning.
Curvature-Conditioned Query modifies only the read step and is composable with any linear-attention backbone, and improves perplexity, zero-shot downstream accuracy, S-NIAH retrieval at and beyond the training context, length-extrapolation perplexity from 4K to 20K, and LongBench accuracy.
TIEM, a timestamp-gated framework with three coordinated components: an Event-Evidence Hypergraph (EEH) for timestamp-filtered multi-tier retrieval; a Case-based Skill Memory (CSM) for source-tagged temporal skills; and Heterogeneous Evidence-Experience Fusion Reasoning (HEFR) for evidence-experience fusion and predict...
This work proposes to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective and introduces EXP - the first benchmark for explicit evaluation of label projection, thereby reducing confounders and non-determinism in method assessment.
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