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20,463 papers
Entropy-Driven Multimodal Answering (EDMA) is proposed, which uses logical entropy to formalise multimodal QA as an iterative process of entropy reduction and to quantify the contribution of each modality in distinguishing correct from incorrect candidate answers.
Paired Outcome-guided Reasoning Allocation (PORA), which learns this decision by comparing short and long reasoning from the same interaction state by comparing their final quality and total processing to estimate when longer reasoning is valuable.
PROVISO is presented, a controller that decides which action to commit after the agent proposes an action and before it is executed, and commits the action with the highest calibrated value learned from outcomes.
OWP is proposed, a training-free framework that shifts hallucination mitigation from output-level manipulation to conflict-conditioned representation rebalancing and adaptively rebalances fused representations by suppressing misleading priors and restoring ignored evidence before decoding.
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Clinical System One (CS1), a 4-billion-parameter decision model for fast, typed judgments from clinical records, is presented, along with two agentic harnesses in which CS1 works together with a generalist language model.
ReuF2F is introduced, a Lean 4 benchmark of 244 formalized open problems drawn from Research Experiences for Undergraduates (REU), spanning entry-level open research problems across a broad range of mathematical subfields and adopts a standard coding-agent evaluation setting.
This monograph seeks to define physical AI, present its technical landscape, and examine each key technical subfield in depth, and establishes a unified framework for Physical AI: artificial systems that perceive, model, and act upon the physical world through continuous feedback under real-world constraints.
M-Plan is proposed, a closed-loop MAS framework that integrates long-term and short-term memory that enhances agents' performance on multi-turn tasks while maintaining a plug-and-play design and outperforms multiple baseline methods in both accuracy and efficiency.
Results provide preliminary evidence that strategic preference optimization can improve strategic quality while preserving teacher-consistent behavior and compare it with SFT, DPO, GRPO, and GSPO.
It is reported that collaboration buys task performance but not lower hallucination as a rule, and no topology is reliably better than another; it is found that collaboration redistributes what its models can already do; it does not supply what they cannot.
SPEM is a lightweight structure-aware procedural memory framework for efficient skill use in LLM-based agents and achieves the best overall effectiveness--efficiency trade-off, reaching 87.1% ALFWorld success and 38.38% SkillsBench reward.
Improved hybrid planning requires modeling LLM behavior sensitive to predicate semantics rather than developing more structural predictors, and progressively rename predicates while preserving states, transitions, and optimal plans in STRIPS domains with BFS-optimal plan lengths.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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