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Self-evolving LLM agents promise to improve autonomously through continual interaction and learning, reducing their dependence on manually curated supervision. Realizing this promise requires not only updating the agent, but also evolving its training experience as its capabilities change. However, most existing pipeli...
This work proposes MetaPersona, a framework that retrieves task-relevant evidence, constructs literature-derived persona dependency graphs, and samples synthetic populations from empirical priors linking demographics, latent attributes, and outcomes.
This work introduces \method{}, a framework for jailbreaking through text-only continuation interfaces that permit repeated sampling and assistant-prefix continuation, and achieves the highest mean score most comparisons against baselines.
This evaluation characterizes how frontier agents combine source-level execution with application screenshots and graphical interaction to produce verified software changes, and examines performance across domains and task information requirements, alongside the development behaviors associated with successful repairs.
This work uncovers short context additions that redirect 61.4%-73.2% of each decision system's initially correct decisions toward a wrong option fixed in advance, exposing a weakness in current decision models.
How much does the noisy measurement add to learned quantum error mitigation? An accuracy table cannot say, because a model handed circuit structure can score well without reading the measurement at all. QEMScore adds the comparison that can. Each simulated circuit carries an exact ideal answer. The learned mitigator is...
This work recovers the dependency layer from six tool-use, coding, and web corpora and measures its failure-prediction gain over run size, finding structure can pass for signal it does not carry and structure can pass for signal it does not carry.
Diverse Skill Routing is proposed, a diversity-aware reranking framework that uses a Determinantal Point Process to balance relevance and non-redundancy and improves recall and full coverage over a strong pointwise reranking baseline, with larger gains on multi-skill queries.
AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.
This paper proposes specification-grounded alignment, a new alignment paradigm that treats provider-authored model specifications as the primary alignment target rather than abstract principles or static benchmarks, and introduces SpecAlign, a framework that synthesizes alignment data directly from specification docume...
SocialMaze is introduced, a benchmark that organizes six tasks across social deduction games, daily-life interactions, and digital community platforms along three descriptive design axes: deep reasoning, dynamic interaction, and information uncertainty.
A benchmark with tab-and-blank interlocking pieces where geometric constraints provide strong local compatibility requirements that, combined with visual content, yield unambiguous ground truth is introduced, establishing scalable geometric reasoning as an open challenge for vision-language models.
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