Paper Citation Record · LEDGER

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems

As of 15 August 2026, this Paper Citation Record lists 99 of 99 outbound references and 3 inbound Pith citation observations for arXiv:2505.17968.

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

pith.paper-citation-record.v1
2505.17968 v1

Coverage vector

measured 99 of 99 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:40:40.704557Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T20:19:27.650696Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T00:06:37.955246Z

Reference resolution

99 of 99 outbound references displayed

verified exact0
verified fuzzy37
unresolved61
parse uncertain1
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 82402fab-ecce-4773-907f-cd8bb8afa647 · outbound

This paper cites Large-Scale Bandit Problems and KWIK Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Large-Scale Bandit Problems and KWIK Learning

Reference 1

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Observation 50c39405-4d30-48ca-9495-442ea9c4d0ee · outbound

This paper cites Queries and Concept Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Queries and Concept Learning

Reference 2

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Observation 57f7fe63-fce0-45b3-bd70-3443d251a9f7 · outbound

This paper cites Inductive Inference: Theory and Methods.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Inductive Inference: Theory and Methods

Reference 3

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Observation d30d95ee-ceca-4221-a946-e5cbf8dc69cb · outbound

This paper cites Claude 3.5 sonnet.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Claude 3.5 sonnet

Reference 4

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Observation 9693341c-04d0-41a0-9cb9-4c3f94395333 · outbound

This paper cites Toward Efficient Exploration by Large Language Model Agents.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Toward Efficient Exploration by Large Language Model Agents

Reference 5

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Observation fcefdc3a-515d-4d59-93b9-9c562e09f279 · outbound

This paper cites A Markovian Decision Process.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems A Markovian Decision Process

Reference 6

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Observation fc58a8ce-a78f-466e-a746-4cebf820daae · outbound

This paper cites Using cognitive psychology to understand GPT-3.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Using cognitive psychology to understand GPT-3

Reference 7

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Observation d291f60f-f390-4fdc-8cb9-c8ac0b12fa8c · outbound

This paper cites Variational inference: A review for statisticians.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Variational inference: A review for statisticians

Reference 8

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Observation d22695ee-a3ea-41f9-8dd8-bf2b9b39962b · outbound

This paper cites R-MAX – A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems R-MAX – A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning

Reference 9

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Observation 6bc69ef7-5ca6-44d8-8eaf-0c4f7ce9e93c · outbound

This paper cites Language models are few-shot learners.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Language models are few-shot learners

Reference 10

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Observation 43339939-93b2-46e5-a19c-abb7de83b6e5 · outbound

This paper cites Why Do Multi-Agent LLM Systems Fail?.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Why Do Multi-Agent LLM Systems Fail?

Reference 11

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Observation 6ce3290c-0b2a-44f6-8b06-4dd45832900d · outbound

This paper cites Bayesian Experimental Design: A Review.Statistical Science, pp.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Bayesian Experimental Design: A Review.Statistical Science, pp

Reference 12

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Observation 3f6f2e9b-8ebe-4a0e-80b4-0d4cc2750171 · outbound

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

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 13

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Observation 51fed5c1-5bc9-4750-9138-b24393c37def · outbound

This paper cites The first crank of the cultural ratchet: Learning and transmitting concepts through language.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems The first crank of the cultural ratchet: Learning and transmitting concepts through language

Reference 14

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Observation 573ec285-8443-4393-8478-1bca2ad57f2b · outbound

This paper cites CogBench: a large language model walks into a psychology lab.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems CogBench: a large language model walks into a psychology lab

Reference 15

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Observation a02f0fd1-c699-42df-a7ca-9513f1fda469 · outbound

This paper cites The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Reference 16

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Observation c841fc75-f7d6-4854-b798-1586206b5ac9 · outbound

This paper cites Uncertainty, Information, and Sequential Experiments.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Uncertainty, Information, and Sequential Experiments

Reference 17

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Observation 65f83589-7dfb-43e6-8e21-a237ddcfcf42 · outbound

This paper cites PILCO: A Model-Based and Data-Efficient Approach to Policy Search.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems PILCO: A Model-Based and Data-Efficient Approach to Policy Search

Reference 18

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Observation 3e2b302c-9af2-4c54-9c27-023bc5314e69 · outbound

This paper cites Aleatory or Epistemic? Does it Matter? Structural Safety, 31(2):105–112, 2009.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Aleatory or Epistemic? Does it Matter? Structural Safety, 31(2):105–112, 2009

Reference 19

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Observation f610df2c-264d-4db8-a8fc-ce0f5c8e817a · outbound

This paper cites Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective

Reference 20

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Observation 377321d7-611e-4dd4-93f7-db14bbafc4ee · outbound

This paper cites Variational Bayesian optimal experimental design.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Variational Bayesian optimal experimental design

Reference 21

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Observation 5a31fd6a-78f1-4d59-baae-dfc5895de5c9 · outbound

This paper cites Baby steps in evaluating the capacities of large language models.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Baby steps in evaluating the capacities of large language models

Reference 22

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Observation d67b3eaf-3302-4617-a054-1155f531c76c · outbound

This paper cites BoxingGym: Benchmarking progress in automated experimental design and model discovery.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems BoxingGym: Benchmarking progress in automated experimental design and model discovery

Reference 23

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Observation 9a177777-7675-4fca-8622-89383c8e4bff · outbound

This paper cites Amplify scientific discovery with artificial intelligence.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Amplify scientific discovery with artificial intelligence

Reference 24

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Observation 2aaa365c-eb5e-49a1-963f-e3f9dac601e1 · outbound

This paper cites ANOVA: Repeated measures.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems ANOVA: Repeated measures

Reference 25

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Observation b96652ac-dc3f-48b4-b0cb-b936df006f2b · outbound

This paper cites Towards an AI co-scientist.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Towards an AI co-scientist

Reference 26

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Observation 7d509de6-b5d1-4fe6-a570-73c8714ad341 · outbound

This paper cites The Llama 3 Herd of Models.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems The Llama 3 Herd of Models

Reference 27

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Observation e94454b9-3bf0-4669-96f1-ec0efc5c3a10 · outbound

This paper cites Bayes in the Age of Intelligent Machines.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Bayes in the Age of Intelligent Machines

Reference 28

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Observation 796414a5-9386-45a3-9c79-e35aefff5510 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 29

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Observation 997b1a60-ef79-45a8-81af-4733c6156182 · outbound

This paper cites Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?

Reference 30

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Observation c1d76051-ac80-4a40-a808-7ee91b4823a7 · outbound

This paper cites GPT-4o System Card.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems GPT-4o System Card

Reference 31

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Observation 7974c83d-b72e-4146-a36e-208df11b3aa8 · outbound

This paper cites What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?

Reference 32

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Observation 81194d79-d3a4-47e4-882b-f0d6797a95f9 · outbound

This paper cites Using the tools of cognitive science to understand large language models at different levels of analysis.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Using the tools of cognitive science to understand large language models at different levels of analysis

Reference 33

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This paper cites A robust class of context- sensitive languages.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems A robust class of context- sensitive languages

Reference 34

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Observation 1d339c4b-a002-4688-9b16-c4a20db7b5e8 · outbound

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Passive learning of active causal strategies in agents and language models

Reference 35

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Observation 08d6ff58-e230-40a8-8229-997dc742e396 · outbound

This paper cites Structured chain-of-thought prompting for code generation.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Structured chain-of-thought prompting for code generation

Reference 36

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This paper cites Reducing Reinforcement Learning to KWIK Online Regression.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Reducing Reinforcement Learning to KWIK Online Regression

Reference 37

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This paper cites Knows What It Knows: A Framework for Self-Aware Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Knows What It Knows: A Framework for Self-Aware Learning

Reference 38

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Observation 35ebf653-8272-48c8-8b50-723822d32714 · outbound

This paper cites On a measure of the information provided by an experiment.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems On a measure of the information provided by an experiment

Reference 39

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This paper cites Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm

Reference 40

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Observation 1ce2be8f-e194-42a5-87d0-115a6f41818f · outbound

This paper cites Decoupling Exploration and Exploitation for Meta-Reinforcement Learning Without Sacrifices.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Decoupling Exploration and Exploitation for Meta-Reinforcement Learning Without Sacrifices

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Observation e3fe3d14-33ed-45ca-8b75-7ec14e8b2bc8 · outbound

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Large Language Models Assume People are More Rational than We Really are

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This paper cites Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse

Reference 43

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This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT Distillation

Reference 45

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Category learning through active sampling

Reference 46

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Is it better to select or to receive? learning via active and passive hypothesis testing

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:48.147093Z

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Modeling rapid language learning by distilling Bayesian priors into artificial neural networks

Reference 48

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Embers of autoregression show how large language models are shaped by the problem they are trained to solve

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:47.995708Z

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Observation e3be7c28-fb95-48a7-a078-4fd94a17d26a · outbound

This paper cites MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration

Reference 50

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Sparks of Science: Hypothesis Generation Using Structured Paper Data

Reference 51

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This paper cites (More) Efficient Reinforcement Learning via Posterior Sampling.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems (More) Efficient Reinforcement Learning via Posterior Sampling

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:47.815242Z

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Observation 038b21dc-64a8-4b1e-978d-80f4a4284dc1 · outbound

This paper cites Puterman.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Puterman

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:47.645625Z

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Observation 844a8a34-8b35-471e-8b6e-faea4f154c0c · outbound

This paper cites Towards scientific discovery with generative ai: Progress, opportunities, and challenges.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Towards scientific discovery with generative ai: Progress, opportunities, and challenges

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:47.543996Z

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Observation 61eec6b9-b181-4567-a92f-b729e4834598 · outbound

This paper cites Diversity-Based Inference of Finite Automata.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Diversity-Based Inference of Finite Automata

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:47.335383Z

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Observation e096bed7-de24-4dad-a5fe-528ebfa59b43 · outbound

This paper cites Inference of Finite Automata Using Homing Sequences.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Inference of Finite Automata Using Homing Sequences

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:47.116883Z

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Observation 391f4971-cca2-42ce-b42e-b7e909f1f4f5 · outbound

This paper cites Jagadish, Marvin Mathony, Tobias Ludwig, and Eric Schulz.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Jagadish, Marvin Mathony, Tobias Ludwig, and Eric Schulz

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:36.544531Z

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Observation ac32d513-3360-4f9d-a0a6-e992cb0f6269 · outbound

This paper cites Symbolic metaprogram search improves learning efficiency and explains rule learning in humans.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Symbolic metaprogram search improves learning efficiency and explains rule learning in humans

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:46.746513Z

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Observation b397f9fd-5cb7-4c33-a49c-73948ca38d19 · outbound

This paper cites Trading off Mistakes and Don’t- Know Predictions.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Trading off Mistakes and Don’t- Know Predictions

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:46.416593Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0a039d9-d524-4528-b9c8-acf3dc5d5e92 · outbound

This paper cites Agent Laboratory: Using LLM Agents as Research Assistants.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Agent Laboratory: Using LLM Agents as Research Assistants

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:36.829520Z

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Observation 0c74deb9-8ff3-4bd5-8068-21cb2a079a07 · outbound

This paper cites Active Learning Literature Survey.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Active Learning Literature Survey

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:46.070277Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 439cc0a6-169c-4dfb-a18c-432cc02b15ec · outbound

This paper cites Llm-sr: Scientific equation discovery via programming with large language models.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Llm-sr: Scientific equation discovery via programming with large language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:45.807125Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 28738de6-bb3c-4bfb-8ab9-454ce709f0e5 · outbound

This paper cites Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:37.118461Z

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Observation 7290d399-1b29-42ab-bc4f-f3030c56f46e · outbound

This paper cites To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:37.243258Z

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Observation 971622c1-4ba9-46c2-ab12-73b9b13d43ba · outbound

This paper cites PaperBench: Evaluating AI's Ability to Replicate AI Research.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems PaperBench: Evaluating AI's Ability to Replicate AI Research

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:37.301272Z

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Observation ae21c6d6-7306-4574-a60b-247ea474c416 · outbound

This paper cites An Analysis of Model-based interval estimation for Markov Decision Processes.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems An Analysis of Model-based interval estimation for Markov Decision Processes

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:45.551239Z

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Observation 35f9fa0f-d275-4436-88e0-646ebed51a86 · outbound

This paper cites A Bayesian framework for reinforcement learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems A Bayesian framework for reinforcement learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:45.290247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87f047f3-9fbf-4cf9-b538-640ffa5b6c2a · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:37.537997Z

Source-reported events for the cited work

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Observation 2c3ffde1-9c2f-435d-84b1-6a78dc4c7403 · outbound

This paper cites Integrated architectures for learning, planning, and reacting based on approximating dynamic programming.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Integrated architectures for learning, planning, and reacting based on approximating dynamic programming

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:45.084955Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e35a777f-eb96-4192-b9e3-aacbbb0f762c · outbound

This paper cites Dyna, an integrated architecture for learning, planning, and reacting.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Dyna, an integrated architecture for learning, planning, and reacting

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:37.794791Z

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Observation 58d2fbfd-c0df-4dcc-94c2-ab0efdb77019 · outbound

This paper cites Introduction to Reinforcement Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Introduction to Reinforcement Learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:44.912222Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 028339c6-93e4-4e83-8fe3-50410e086460 · outbound

This paper cites Agnostic KWIK learning and Efficient Approximate Reinforcement Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Agnostic KWIK learning and Efficient Approximate Reinforcement Learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:44.696029Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9f2ec0af-3f1c-48dd-9e1d-f7c7ec585536 · outbound

This paper cites Active exploration in dynamic environments.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Active exploration in dynamic environments

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:44.516965Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3af49d99-a977-495d-b799-48b19f383d99 · outbound

This paper cites Exploring Compact Reinforcement-Learning Representations with Linear Regression.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Exploring Compact Reinforcement-Learning Representations with Linear Regression

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:44.353743Z

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Observation 777acb38-9e0d-463b-9206-3e95660ebe3c · outbound

This paper cites Scientific Discovery in the Age of Artificial Intelligence.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Scientific Discovery in the Age of Artificial Intelligence

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:44.215739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b6f8b0d4-7ab8-4cd1-8299-3f881701ac04 · outbound

This paper cites Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:38.414916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92d3dbcd-db87-46df-84ab-0cd4783b1d68 · outbound

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

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Chain-of-thought prompting elicits reasoning in large language models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:38.504795Z

Source-reported events for the cited work

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Observation eabe149f-13cf-467b-9b42-111b84afa1ed · outbound

This paper cites An explanation of in-context learning as implicit Bayesian inference.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems An explanation of in-context learning as implicit Bayesian inference

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:44.071384Z

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Observation ba0347c7-6571-4e20-9d66-1b705672e0b1 · outbound

This paper cites Piantadosi.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Piantadosi

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:43.980017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c6fa9a29-8b5d-4f61-90c6-ee414cc17cf0 · outbound

This paper cites On Benchmarking Human-Like Intelligence in Machines.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems On Benchmarking Human-Like Intelligence in Machines

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:38.848439Z

Source-reported events for the cited work

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Observation 1d8b21e7-8c45-47e4-8a79-bc16e3fb95a8 · outbound

This paper cites People use fast, goal-directed simulation to reason about novel games.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems People use fast, goal-directed simulation to reason about novel games

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:38.988889Z

Source-reported events for the cited work

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Observation 5d74f7bd-c2f4-43c7-943a-c49c357804a9 · outbound

This paper cites Eliciting the Priors of Large Language Models using Iterated In-Context Learning.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Eliciting the Priors of Large Language Models using Iterated In-Context Learning

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:39.080062Z

Source-reported events for the cited work

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Observation cd18fa2d-5a8c-4ce0-b329-e413ad30db57 · outbound

This paper cites Incoherent Probability Judgments in Large Language Models.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Incoherent Probability Judgments in Large Language Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:39.192857Z

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Unavailable: canonical work link unavailable.

Observation fdf9af22-6755-463d-9fae-66dd7bb25d63 · outbound

This paper cites Provide a thorough reasoning before performing the action.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Provide a *thorough reasoning* before performing the action

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:43.903505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b26e766-d0ee-4bc4-9852-8428de509d9a · outbound

This paper cites [5 point].

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems [5 point]

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:43.769540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 402086e6-04fc-44ae-b9b7-d37854a3b984 · outbound

This paper cites You will then output a score based on a set of assessment criteria.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems You will then output a score based on a set of assessment criteria

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:43.565586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2bd083ca-20b8-4d2e-ab63-70b2b9c2bba2 · outbound

This paper cites [3 points].

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems [3 points]

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:43.409550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 02f91f73-9274-43e4-bdab-e63008affb85 · outbound

This paper cites an unresolved cited work.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:40:43.238105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e0ab5211-cae1-4849-b8df-a67c01c50722 · outbound

This paper cites an unresolved cited work.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:40:43.073924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f8de0d1e-0dd6-4bfa-82fc-2dc4efb0b8c7 · outbound

This paper cites (Note that there will be multiple a_i 's.).

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems (Note that there will be multiple a_i 's.)

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:42.903921Z

Source-reported events for the cited work

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Observation 7dde8e8c-00b8-4453-9b44-b0a605a0a75b · outbound

This paper cites an unresolved cited work.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:40:42.815299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 091783d3-1335-4dc9-b82b-376aa5d26901 · outbound

This paper cites an unresolved cited work.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:40:42.686738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1e0f90f9-c980-41b2-b8ff-5de72fd2854d · outbound

This paper cites The score for this bullet should be the accuracy percentage times the total allocated 6 points [6 points].

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems The score for this bullet should be the accuracy percentage times the total allocated 6 points [6 points]

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:42.535878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c06d16d-e10c-4406-b32f-7e9fbd3d8dd9 · outbound

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Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:40:42.440082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1161a586-9ef7-4cc8-9640-82a58d2a0058 · outbound

This paper cites Win by connecting 3 stones in a column.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Win by connecting 3 stones in a column

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:42.304484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a46dffa2-aae7-4f09-8573-a72c4beaa0a2 · outbound

This paper cites an unresolved cited work.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 96

Resolution: parse uncertain; raw_fallback, observed 2026-08-07T14:40:42.165301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa59d39a-2c8a-4d6f-8c50-875550e5f662 · outbound

This paper cites an unresolved cited work.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:40:41.981797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 93d5b542-c867-4482-b680-55190bd4d052 · outbound

This paper cites an unresolved cited work.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:40:41.848711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 99ce0b0d-b0af-4510-9b0a-d475301ec7dc · outbound

This paper cites AAA", "BBB.

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems AAA", "BBB

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:40:41.698993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 3a5a7a26-a078-46cd-b7f9-9315ee619eec · inbound

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems

Reference 293

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:23:15.430452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0ecc73d-9b18-4cec-9108-f6ee09342aa6 · inbound

CausalGame: Benchmarking Causal Thinking of LLM Agents in Games cites this paper.

CausalGame: Benchmarking Causal Thinking of LLM Agents in Games Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-11T20:19:27.650696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bddc95b-cf75-4d8b-bd2c-3374077b922a · inbound

Mining Workflow Graphs for Black-Box Boundary Testing of Conversational LLM Agents cites this paper.

Mining Workflow Graphs for Black-Box Boundary Testing of Conversational LLM Agents Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T00:06:37.956842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.