Paper Citation Record · LEDGER

Reasoning Capabilities of Large Language Models on Dynamic Tasks

As of 17 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2505.10543.

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

pith.paper-citation-record.v1
2505.10543 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:12:01.571261Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:36:44.033596Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:25:46.619265Z

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy4
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 775d0281-04ef-4006-b35f-d0d7237facb2 · outbound

This paper cites Language mod- els are few-shot learners,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Language mod- els are few-shot learners,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.466773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca51aa6e-73d8-4458-87c9-cb654602f008 · outbound

This paper cites Reflex- ion: Language agents with verbal reinforcement learning,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Reflex- ion: Language agents with verbal reinforcement learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.471784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d6b2e2a-80bf-4586-a364-39ea9594b2ec · outbound

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

Reasoning Capabilities of Large Language Models on Dynamic Tasks ReAct: Synergizing Reasoning and Acting in Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.477653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ff6ec71-be91-42a3-8f17-8315825a5e20 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

Reasoning Capabilities of Large Language Models on Dynamic Tasks The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.483633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 397d00e7-3ba5-429e-a182-d106554c23df · outbound

This paper cites Text-based games as a challenging benchmark for large language models,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Text-based games as a challenging benchmark for large language models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:01.979481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f7aa33f-e1db-4eb1-b571-debb7e5e8640 · outbound

This paper cites Training language models to follow instructions with human feedback,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Training language models to follow instructions with human feedback,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.493247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba4a0d14-a25e-4426-9429-0b67fc784714 · outbound

This paper cites Prompt programming for large language models: Beyond the few-shot paradigm,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Prompt programming for large language models: Beyond the few-shot paradigm,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.498450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 564526f6-4b44-4d2e-9eb5-c1230ed93105 · outbound

This paper cites SmartPlay: A Benchmark for LLMs as Intelligent Agents.

Reasoning Capabilities of Large Language Models on Dynamic Tasks SmartPlay: A Benchmark for LLMs as Intelligent Agents

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.502630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac619244-a0bb-40b6-a592-7810617ec795 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Chain-of-thought prompting elicits reasoning in large language models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.507211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50feaa8f-be13-44e6-a508-f20848d88d13 · outbound

This paper cites Self-refine: Iter- ative refinement with self-feedback,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Self-refine: Iter- ative refinement with self-feedback,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.511339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0259a033-897e-4c0e-8a1c-2a357cedce90 · outbound

This paper cites Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.515887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6cafd80-2a29-4413-8728-be1593027a34 · outbound

This paper cites AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models.

Reasoning Capabilities of Large Language Models on Dynamic Tasks AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:12:01.733559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 377af352-bb2d-4d06-9771-d332ad7d3e72 · outbound

This paper cites Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.524931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b535f04-ce16-415b-bc38-e7c19f0013d8 · outbound

This paper cites Mental Modeling of Reinforcement Learning Agents by Language Models.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Mental Modeling of Reinforcement Learning Agents by Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.529561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62adbb95-fe10-4f37-be0c-6b79d85bdc0a · outbound

This paper cites EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers.

Reasoning Capabilities of Large Language Models on Dynamic Tasks EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.534625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db7d2981-7220-4eb8-b4f8-d329ee8e128b · outbound

This paper cites Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:01.925131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d576973-9a70-4cc9-9ba4-d9da422eac3b · outbound

This paper cites Focused transformer: Contrastive training for context scaling,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Focused transformer: Contrastive training for context scaling,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:01.909695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1a413d7-8a89-420c-928b-dfd617713924 · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Lost in the Middle: How Language Models Use Long Contexts

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.547721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 528de17c-6008-4c33-b393-b4b9f20b0797 · outbound

This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.552813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 937adfb6-0f8f-40dc-aed3-cc8084c7ca7d · outbound

This paper cites Chain of thought- lessness? an analysis of cot in planning,.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Chain of thought- lessness? an analysis of cot in planning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:12:01.819066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b30c080-4519-49ea-ae4d-2a4feb7e57d4 · outbound

This paper cites Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?.

Reasoning Capabilities of Large Language Models on Dynamic Tasks Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.562040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1c75472-7ada-44f0-b4fb-ed9f59678d52 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Reasoning Capabilities of Large Language Models on Dynamic Tasks HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.566767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0664dfa-eb55-42ae-9c61-6d5a51f64d30 · outbound

This paper cites BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games.

Reasoning Capabilities of Large Language Models on Dynamic Tasks BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:12:01.571261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 82031c4c-49ec-4ff8-99a9-b8e562076058 · inbound

Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform cites this paper.

Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform Reasoning Capabilities of Large Language Models on Dynamic Tasks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:25:46.621005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.