Paper Citation Record · LEDGER

Uncertainty-Aware Clarification in LLM Agents with Information Gain

As of 16 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:2606.03135.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.03135 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:27:18.135089Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T22:25:22.238456Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact15
verified fuzzy0
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 494d545d-5279-4cba-ad51-8bc566a6b083 · outbound

This paper cites AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.

Uncertainty-Aware Clarification in LLM Agents with Information Gain AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.643488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 866878e0-d216-40dc-b2e7-b446c4ad2581 · outbound

This paper cites BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design.

Uncertainty-Aware Clarification in LLM Agents with Information Gain BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T02:56:29.639487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 760a1c54-bb11-4b2d-a669-3e49ed95dde2 · outbound

This paper cites A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges.

Uncertainty-Aware Clarification in LLM Agents with Information Gain A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:56:29.624124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b925e022-1f30-4dfc-b186-a399e6097b9f · outbound

This paper cites AgentBench: Evaluating LLMs as Agents.

Uncertainty-Aware Clarification in LLM Agents with Information Gain AgentBench: Evaluating LLMs as Agents

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T02:56:29.626993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2a8a97d4-cb4c-4ae7-991b-5956fc61d87a · outbound

This paper cites AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios.

Uncertainty-Aware Clarification in LLM Agents with Information Gain AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:56:29.606851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 47deeecc-fb39-44a1-802b-0c02b61eec81 · outbound

This paper cites Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information.

Uncertainty-Aware Clarification in LLM Agents with Information Gain Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.635780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0108f10e-0a31-4fcf-a5c4-c06e61a4d1eb · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Uncertainty-Aware Clarification in LLM Agents with Information Gain DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.639172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6f8306ef-26aa-4bb7-bf7e-add58c6e086f · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Uncertainty-Aware Clarification in LLM Agents with Information Gain HybridFlow: A Flexible and Efficient RLHF Framework

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.610883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 01b64b55-a00c-42d8-9f50-cb9ff009d0d7 · outbound

This paper cites LLM-as-a-Judge & Reward Model: What They Can and Cannot Do.

Uncertainty-Aware Clarification in LLM Agents with Information Gain LLM-as-a-Judge & Reward Model: What They Can and Cannot Do

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:56:29.647395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2a283298-3829-4baf-833c-d6a7f6214c73 · outbound

This paper cites Structured Uncertainty guided Clarification for LLM Agents.

Uncertainty-Aware Clarification in LLM Agents with Information Gain Structured Uncertainty guided Clarification for LLM Agents

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.593607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c3e050a-6727-4279-81a0-582d72dc2a25 · outbound

This paper cites Information gain-based policy optimization: A simple and effective approach for multi-turn llm agents.

Uncertainty-Aware Clarification in LLM Agents with Information Gain Information gain-based policy optimization: A simple and effective approach for multi-turn llm agents

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:56:29.627794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 34d7d60e-d1f6-4cf0-a604-1e4a3d2b23b2 · outbound

This paper cites TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks.

Uncertainty-Aware Clarification in LLM Agents with Information Gain TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T02:56:29.600584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3f31cf1d-8d80-4c7b-a372-50872c4ede47 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Uncertainty-Aware Clarification in LLM Agents with Information Gain ReAct: Synergizing Reasoning and Acting in Language Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.613524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2bf1adeb-7d9e-4852-a949-e973ca5daefc · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

Uncertainty-Aware Clarification in LLM Agents with Information Gain $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.649622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9b363450-2977-4581-b630-675b7cee4259 · outbound

This paper cites Survey on Evaluation of LLM-based Agents.

Uncertainty-Aware Clarification in LLM Agents with Information Gain Survey on Evaluation of LLM-based Agents

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.619587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 04f24a24-38a5-41c3-b5c4-14189bb20ef7 · outbound

This paper cites CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval.

Uncertainty-Aware Clarification in LLM Agents with Information Gain CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:56:29.603019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 757101ff-56b8-47a3-b893-365186da8b89 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Uncertainty-Aware Clarification in LLM Agents with Information Gain DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.616555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8b13a1d0-d1c9-4a8c-a7b7-9c684e3f01c3 · outbound

This paper cites Easytool: Enhancing llm-based agents with concise tool instruction.

Uncertainty-Aware Clarification in LLM Agents with Information Gain Easytool: Enhancing llm-based agents with concise tool instruction

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T10:27:18.135089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c145bb6-4de9-473d-8b86-77411f5bbfb8 · outbound

This paper cites K., and Chua, T.- S.

Uncertainty-Aware Clarification in LLM Agents with Information Gain K., and Chua, T.- S

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T10:27:18.135089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 086da257-9598-408c-9060-adcd1a6cd973 · outbound

This paper cites From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?.

Uncertainty-Aware Clarification in LLM Agents with Information Gain From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:56:29.642407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6609cc76-dbb8-4d26-b4d3-c4303a20c823 · outbound

This paper cites Your name is.

Uncertainty-Aware Clarification in LLM Agents with Information Gain Your name is

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T10:27:18.135089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 468199d7-0e6c-4a9f-b480-408222a55551 · inbound

Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs cites this paper.

Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs Uncertainty-Aware Clarification in LLM Agents with Information Gain

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T22:25:22.238456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.