Human Feedback and Eval Paper Explorer

Match reason: Keyword overlap 2/2 across title and protocol fields.

• Recent LLM-based agents often place world modeling, planning, and reflection inside a single language model loop.
• We introduce a declared reflective runtime protocol that externalizes agent state, confidence signals, guarded actions, and hypothetical transitions into inspectable runtime structure.

Match reason: Keyword overlap 2/2 across title and protocol fields.

• We highlight three primary challenges for LLMs in mental health - lack of high quality interpretable and knowledge grounded training data; training paradigms restricted to core capabilities, and evaluation of multi turn dialogue settings.
• Addressing it, we present oMind framework which includes training and aligning LLM agents for diverse capabilities including conversations; high quality ~164k multi-task SFT dataset, as a result of our generation pipeline based on…

Match reason: Keyword overlap 2/2 across title and protocol fields.

• We propose a novel framework that uses a Large Language Model (LLM) to dynamically generate a curriculum over available actions, enabling the agent to incorporate each action individually.
• The curriculum-based approach increases the DQN agent's average win rate from 43.97% to 47.41%, reduces the average bust rate from 32.9% to 28.0%, and accelerates the overall workflow by over 74%, with the agent's full training completing…

Match reason: Keyword overlap 2/2 across title and protocol fields.

• In human evaluations conducted by native speakers, Voxtral TTS is preferred for multilingual voice cloning due to its naturalness and expressivity, achieving a 68.4\% win rate over ElevenLabs Flash v2.5.

Match reason: Keyword overlap 2/2 across title and protocol fields.

• Across diverse chemical reasoning benchmarks, LatentChem achieves a 59.88\% non-tie win rate over strong CoT-based baselines on ChemCoTBench, while delivering a 10.84\times average reduction in reasoning overhead.

Match reason: Keyword overlap 2/2 across title and protocol fields.

• To address this challenge, we introduce Robust Preference Selection (RPS), a post-hoc, training-free method by leveraging directional neighborhood consensus.
• Comprehensive experiments across three distinct alignment paradigms (DPA, DPO, and SFT) demonstrate that RPS consistently improves robustness against this baseline, achieving win rates of up to 69% on challenging preferences from…

Match reason: Keyword overlap 2/2 across title and protocol fields.

• The task is turn-based, multi-agent, and physically grounded, posing significant challenges due to its long-horizon dependencies, tight inter-agent coupling, and the underactuated dynamics of quadrotors.

Match reason: Keyword overlap 2/2 across title and protocol fields.

• Evaluations show STAR-LDM significantly outperforms similar-sized models on language understanding benchmarks and achieves >70\% win rates in LLM-as-judge comparisons for narrative coherence and commonsense reasoning.

Match reason: Keyword overlap 2/2 across title and protocol fields.

• We provide a comprehensive suite of tasks ranging from single-drone drills to multi-drone cooperative and competitive tasks, accompanied by baseline evaluations of representative reinforcement learning (RL), multi-agent reinforcement…
• Simulation results show that on-policy RL methods outperform off-policy methods in single-agent tasks, but both approaches struggle in complex tasks that combine motion control and strategic play.

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