OPPO derives token-level advantages for LLM RL via Bayesian recursion on oracle signals, recovering prior distillation methods as a special case and showing gains on math and code benchmarks.
Advancing reasoning in large language models: Promising methods and approaches
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UNVERDICTED 4representative citing papers
PRJA achieves 83.6% average success injecting harmful content into LRM reasoning chains on five QA datasets without altering final answers.
A framework encodes observed trajectories and HD maps into tokens for frozen LLMs to perform spatio-temporal reasoning and predict future vehicle paths with a linear decoder.
ARTIST couples agentic reasoning with outcome-based reinforcement learning to let LLMs autonomously invoke tools in multi-turn chains, reporting up to 22% gains on math and function-calling benchmarks.
citing papers explorer
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OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning
OPPO derives token-level advantages for LLM RL via Bayesian recursion on oracle signals, recovering prior distillation methods as a special case and showing gains on math and code benchmarks.
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Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing
PRJA achieves 83.6% average success injecting harmful content into LRM reasoning chains on five QA datasets without altering final answers.
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Frozen LLMs as Map-Aware Spatio-Temporal Reasoners for Vehicle Trajectory Prediction
A framework encodes observed trajectories and HD maps into tokens for frozen LLMs to perform spatio-temporal reasoning and predict future vehicle paths with a linear decoder.
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Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning
ARTIST couples agentic reasoning with outcome-based reinforcement learning to let LLMs autonomously invoke tools in multi-turn chains, reporting up to 22% gains on math and function-calling benchmarks.