Research desk

The papers behind how we build

An annotated reading list. Every entry is credited to its original authors, with a note on what it changes once the idea meets a real system.

22 entries · RSS feed

All entries

Newest Oldest A to Z

All Foundations3 Retrieval & RAG4 Reasoning5 Agents & Tool Use6 Adaptation3 Alignment3 Evaluation5

Any 20239 20226 20212 20203 20181 20171

5 entries

2023NeurIPS 2023PublishedCurated
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Yao et al.
Generalises chain-of-thought into a search tree. The model proposes multiple partial solutions, evaluates them against each other, and can back out of a branch that is not working, rather than committing to one linear line of reasoning.
Reasoning Agents & Tool Use
arXiv
2023NeurIPS 2023PublishedCurated
Reflexion: Language Agents with Verbal Reinforcement Learning
Shinn et al.
Instead of updating weights, the agent writes down what went wrong after a failed attempt and carries that reflection into the next attempt. The reflection is stored in natural language and used as additional context, so no gradient step is required.
Agents & Tool Use Reasoning
arXiv
2022ICLR 2023PublishedCurated
ReAct: Synergizing Reasoning and Acting in Language Models
Yao et al.
Interleaves verbal reasoning traces with tool actions, so the model thinks about what to do next, does it, then reasons about what came back. The reasoning trace also becomes an auditable record of why the agent took each step.
Agents & Tool Use Reasoning
arXiv
2022ICLR 2023PublishedCurated
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang et al.
Samples several independent reasoning paths for the same question and takes the most common answer, instead of trusting a single greedy chain. The intuition is that a correct derivation can be reached many ways while a wrong one is usually reached once.
Reasoning
arXiv
2022NeurIPS 2022PublishedCurated
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei et al.
Shows that asking a model to write out intermediate reasoning steps before answering substantially improves multi-step arithmetic, commonsense and symbolic reasoning, and that the behaviour appears without any fine-tuning at sufficient model scale.
Reasoning
arXiv

Browse by topic

Foundations

The architectures and pre-training results everything else stands on.

3 entries

Retrieval & RAG

Grounding a model in documents you actually own.

4 entries

Reasoning

Getting a model to show its work, and checking the work.

5 entries

Agents & Tool Use

Models that call tools, hold state, and finish multi-step jobs.

6 entries

Adaptation

Changing what a model does without retraining all of it.

3 entries

Alignment

Steering outputs toward what a person would accept.

3 entries

Evaluation

How you know it works before a customer finds out it does not.

5 entries

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Research desk

The papers behind how we build

An annotated reading list. Every entry is credited to its original authors, with a note on what it changes once the idea meets a real system.

22 entries · RSS feed

All entries

Newest Oldest A to Z

All Foundations3 Retrieval & RAG4 Reasoning5 Agents & Tool Use6 Adaptation3 Alignment3 Evaluation5

Any 20239 20226 20212 20203 20181 20171

5 entries

2023NeurIPS 2023PublishedCurated
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Yao et al.
Generalises chain-of-thought into a search tree. The model proposes multiple partial solutions, evaluates them against each other, and can back out of a branch that is not working, rather than committing to one linear line of reasoning.
Reasoning Agents & Tool Use
arXiv
2023NeurIPS 2023PublishedCurated
Reflexion: Language Agents with Verbal Reinforcement Learning
Shinn et al.
Instead of updating weights, the agent writes down what went wrong after a failed attempt and carries that reflection into the next attempt. The reflection is stored in natural language and used as additional context, so no gradient step is required.
Agents & Tool Use Reasoning
arXiv
2022ICLR 2023PublishedCurated
ReAct: Synergizing Reasoning and Acting in Language Models
Yao et al.
Interleaves verbal reasoning traces with tool actions, so the model thinks about what to do next, does it, then reasons about what came back. The reasoning trace also becomes an auditable record of why the agent took each step.
Agents & Tool Use Reasoning
arXiv
2022ICLR 2023PublishedCurated
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang et al.
Samples several independent reasoning paths for the same question and takes the most common answer, instead of trusting a single greedy chain. The intuition is that a correct derivation can be reached many ways while a wrong one is usually reached once.
Reasoning
arXiv
2022NeurIPS 2022PublishedCurated
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei et al.
Shows that asking a model to write out intermediate reasoning steps before answering substantially improves multi-step arithmetic, commonsense and symbolic reasoning, and that the behaviour appears without any fine-tuning at sufficient model scale.
Reasoning
arXiv