Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts
Paper • 2509.04500 • Published • 5
ChengQ
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Natural Language Processing
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Context Engineering for Trustworthiness: Rescorla Wagner Steering Under
Mixed and Inappropriate Contexts authored a paper about 4 hours ago
EscapeBench: Towards Advancing Creative Intelligence of Language Model
Agents authored a paper about 4 hours ago
The Right Time Matters: Data Arrangement Affects Zero-Shot
Generalization in Instruction TuningOrganizations
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authored 20
papers about 4 hours ago
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents
Paper • 2412.13549 • Published
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning
Paper • 2406.11721 • Published
Self-Improving LLM Agents at Test-Time
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Atomic Reasoning for Scientific Table Claim Verification
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ShortageSim: Simulating Drug Shortages under Information Asymmetry
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xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning
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CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents
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LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering
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JustRL: Scaling a 1.5B LLM with a Simple RL Recipe
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From Word to World: Can Large Language Models be Implicit Text-based World Models?
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Geometric-Disentangelment Unlearning
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ISACL: Internal State Analyzer for Copyrighted Training Data Leakage
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Agentic Reasoning for Large Language Models
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Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs
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Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data
Paper • 2602.21320 • Published • 12
How Far Can Unsupervised RLVR Scale LLM Training?
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Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
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PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning
Paper • 2601.11957 • Published • 3
Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning
Paper • 2510.01932 • Published