Contextual Information Policy Optimization for Search Agents

• URL: http://arxiv.org/abs/2608.06128v3
• Date: Tue, 11 Aug 2026 02:08:54 GMT
• Title: Contextual Information Policy Optimization for Search Agents
• Abstract summary: We propose Contextual Information Policy Optimization (CIPO) to align policy optimization with external evidence use.<n>CIPO assigns turn-level credit to reasoning actions influenced by retrieved information, while combining this evidence-use signal with a global outcome reward to preserve answer correctness.<n>Experiments show that CIPO reduces the prevalence of prior-driven reasoning and achieves excellent performance on most tasks.
• Score: 25.589979477350095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use external evidence during multi-step reasoning. For knowledge-intensive tasks involving complex or evolving information, their reliability depends not only on retrieving relevant evidence but also on using it to guide subsequent reasoning. However, existing methods primarily reward final-answer correctness or intermediate progress, without directly assessing whether post-retrieval actions are grounded in the retrieved evidence. This misalignment encourages prior-driven reasoning: agents form conclusions based on internal knowledge and use retrieval mainly to confirm them, resulting in confirmation bias and inefficient evidence use. To address this issue, we propose Contextual Information Policy Optimization (CIPO), an evidence-oriented reinforcement learning framework that explicitly aligns policy optimization with external evidence use. CIPO assigns dense, turn-level credit to reasoning actions influenced by retrieved information, while combining this evidence-use signal with a global outcome reward to preserve answer correctness. With this manner, CIPO discourages evidence-detached guesses and promotes reasoning trajectories in which retrieved facts can guide or revise subsequent reasoning. Importantly, CIPO requires neither human process annotations nor an additional reward model. Extensive experiments on seven in-domain and out-of-domain benchmarks show that CIPO reduces the prevalence of prior-driven reasoning and achieves excellent performance on most tasks.

Related papers

• SAFE-G: Structure-aware Faithful Evidence-guided Generation for Knowledge-based Visual Question Answering [8.538563236020947]
  SAFE-G is a structure-Aware Faithful Evidence-guided Generation framework.<n>It enables precise evidence localization and trustworthy reasoning.<n>It outperforms prior methods by a margin of 8.9% and 3.5%.
  arXiv  Detail & Related papers  (2026-08-22T06:34:53Z)
• RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation [14.301653810918713]
  Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems.<n>We propose RRCM, a ranking-driven retrieval-and-reasoning framework for LLM-based agentic recommendation.
  arXiv  Detail & Related papers  (2026-05-08T02:07:30Z)
• FRESCO: Benchmarking and Optimizing Re-rankers for Evolving Semantic Conflict in Retrieval-Augmented Generation [73.22935457705057]
  We introduce FRESCO, a benchmark for evaluating re-rankers in temporally dynamic contexts.<n>By pairing recency-seeking queries with historical Wikipedia revisions, FRESCO tests whether re-rankers can prioritize factually recent evidence while maintaining semantic relevance.<n>Our evaluation reveals a consistent failure mode across existing re-rankers: a strong bias toward older, semantically rich documents, even when they are factually obsolete.
  arXiv  Detail & Related papers  (2026-04-14T17:04:25Z)
• Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval [60.25608870901428]
  Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs)<n>We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source robustness.
  arXiv  Detail & Related papers  (2026-03-05T18:42:51Z)
• Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking [47.47518672198846]
  Misinformation spreading over the Internet poses a significant threat to both societies and individuals.<n>Previous methods rely on semantic and social-contextual patterns learned from training data.<n>We propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence.
  arXiv  Detail & Related papers  (2026-02-27T19:29:01Z)
• Optimizing Agentic Reasoning with Retrieval via Synthetic Semantic Information Gain Reward [24.738836592075927]
  We introduce a unified framework that incentivizes effective information seeking via a synthetic semantic information gain reward.<n>Experiments across seven question-answering benchmarks demonstrate that InfoReasoner consistently outperforms strong retrieval-augmented baselines.<n>Our work provides a theoretically grounded and scalable path toward agentic reasoning with retrieval.
  arXiv  Detail & Related papers  (2026-01-31T18:15:50Z)
• Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning [52.144281362465996]
  We propose EAPO (Evidence-Augmented Policy Optimization) to apply Reinforcement Learning to long-context scenarios.<n>We first establish the Evidence-Augmented Reasoning paradigm, validating via Tree-Structured Evidence Sampling.<n>We then introduce a specialized RL algorithm where a reward model computes a Group-Relative Evidence Reward.<n>To sustain accurate supervision throughout training, we further incorporate an Adaptive Reward-Policy Co-Evolution mechanism.
  arXiv  Detail & Related papers  (2026-01-15T11:40:57Z)
• Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards [60.0970117192627]
  Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents.<n>Existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process.<n>We propose textbfCitation-aware RL Rewards (CaRR), a fine-grained reward framework for deep search agents.
  arXiv  Detail & Related papers  (2026-01-09T18:57:53Z)
• PoU: Proof-of-Use to Counter Tool-Call Hacking in DeepResearch Agents [24.502121097996294]
  Retrieval-augmented generation (RAG) agents extend large language models with autonomous information-seeking capabilities through external tools.<n>We identify a previously overlooked failure mode, Tool-Call Hacking, where agents inflate reward signals by issuing superficially correct tool calls.<n>We propose Proof-of-Use (PoU), an evidence-grounded RL framework that enforces verifiable causal links between retrieved evidence, reasoning traces, and final answers.
  arXiv  Detail & Related papers  (2025-10-13T02:45:37Z)
• Improving Context Fidelity via Native Retrieval-Augmented Reasoning [35.50952279309109]
  Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information.<n>We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities.<n>Our method requires limited labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain.
  arXiv  Detail & Related papers  (2025-09-17T04:28:07Z)
• ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation [82.54090885503287]
  Retrieval-Augmented Generation augments Large Language Models with external knowledge to improve factuality.<n>Existing RAG systems fail to extract and integrate the key clues needed to support faithful and interpretable reasoning.<n>We propose ClueAnchor, a novel framework for enhancing RAG via clue-anchored reasoning exploration and optimization.
  arXiv  Detail & Related papers  (2025-05-30T09:18:08Z)
• From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification [118.03466985807331]
  We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence.<n>We introduce the feedback-based evidence retriever(FER) that optimize the evidence retrieval process by incorporating feedback from the claim verifier.
  arXiv  Detail & Related papers  (2023-10-18T02:59:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.