Paper Citation Record · LEDGER

AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2505.16944.

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

pith.paper-citation-record.v1
2505.16944 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T12:21:00.066780Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:18:33.541374Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation fc2f4517-1872-4a63-960a-5c081147e530 · inbound

Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following cites this paper.

Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T06:56:01.546219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0c9fa60a-ca55-4835-8ce9-21d885d6028f · inbound

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering cites this paper.

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T12:21:00.066780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2c3ea5d-9691-4407-a4fb-898ca0a234d5 · inbound

UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization and Distillation cites this paper.

UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization and Distillation AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T03:07:44.406753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0fe5729-db7a-426c-a6a4-399768cd8de1 · inbound

SEIF: Self-Evolving Reinforcement Learning for Instruction Following cites this paper.

SEIF: Self-Evolving Reinforcement Learning for Instruction Following AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:20:55.896543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8c58bbf3-a6ce-454e-a644-574f5df195d0 · inbound

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems cites this paper.

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.515059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1a87ecee-0c1a-413e-9efb-2a6f6f06ac2e · inbound

A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback cites this paper.

A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-20T09:23:10.615029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7d556ebd-4c18-473b-8be4-ed6554e9953e · inbound

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments cites this paper.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.609311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 01b7c6f0-63e1-42c7-a09c-b2b95494937e · inbound

Selective QA over Conflicting Multi-Source Personal Memory: A Diagnostic Testbed and Method Comparison cites this paper.

Selective QA over Conflicting Multi-Source Personal Memory: A Diagnostic Testbed and Method Comparison AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:53:13.341187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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

Uncertainty-Aware Clarification in LLM Agents with Information Gain cites this paper.

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 2026-08-05T06:32:48.257954+00:00.

Observation 2d5e20ff-65c2-4c5d-b127-97d92cb185db · inbound

CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments cites this paper.

CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 64

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T13:16:58.907822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ad1aed7f-9dbc-4336-8c32-51dd99256bdc · inbound

EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery cites this paper.

EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:18:33.543087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 92056536-b256-4f56-8023-b70188111921 · inbound

Obey, Diverge, Collapse: Blind Obedience to Incorrect Instructions Drives Code LLMs to Irrecoverable Code Semantic Collapse cites this paper.

Obey, Diverge, Collapse: Blind Obedience to Incorrect Instructions Drives Code LLMs to Irrecoverable Code Semantic Collapse AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-11T17:45:46.872944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 543eb8a8-7eae-49d6-88b4-e0cbab2b4dfc · inbound

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios cites this paper.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 87

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.848292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.