Catalog Systems Recent method papers — theorized, no distinct product Buffer of Thoughts

Buffer of Thoughts

Thought-augmented reasoning with LLMs. NeurIPS 2024.

Active · Verified 2026-10-23 · fresh as of 2026-10-23 · 23 cited facts, 0 with a third-party source · Suggest a correction

At a glance

Type
Thought-augmented reasoning paper fix
Tier
T3
Section
Recent method papers — theorized, no distinct product
Created
2024-06-06 (arxiv 2406.04271 submitted June 6 2024; NeurIPS 2024 Spotlight) paper fix
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Pricing
not applicable — not commercial fix
Funding
not applicable — not commercial fix
Freshness
2026-10-23 fix
Layer
agent-infra repo fix

Memory taxonomy

storage
kv
retrieval
similarity
persistence
session
update
append-only
unit
chunk
governance
inspectable
conflict
llm-arbitrate

When to use

Optimised for: not applicable - research paper

Anti-fit: not applicable - research paper

Pros & cons

Pros

Thought-augmented reasoning — caches and reuses high-level thoughts across queries; NeurIPS 2024.

Cons

Buffer maintenance overhead; thought-quality bounds reuse value.

Claims & capabilities

Stores high-level thought-templates distilled from problem-solving; buffer-manager dynamically updates the meta-buffer at 12% of the compute of competing methods. Headline: +11% on Game of 24, +20% on Geometric Shapes, +51% on Checkmate-in-One over previous SOTA; baseline: previous SOTA prompting/reasoning methods; primary dataset: 10 reasoning-intensive tasks including Game of 24, Geometric Shapes, Checkmate-in-One.

Technical surface

API surface
not applicable — research paper
Backend storage
not applicable — research paper
Deployment
not applicable — not a deployable product
Embedding model
not applicable — research paper
Multi-tenancy
not applicable — research paper
MCP
not applicable — research paper, no deployed product
A2A
not applicable — research paper, no deployed product
OpenTelemetry
not applicable — research paper, no deployed product

Similar systems

Other recent method papers — theorized, no distinct product in the catalog, ranked by inbound references.

  • Compressive Transformer T3

    Maintains recent states in full resolution while compressing older memories with learned compression functions. DeepMind.

  • MemGPT v2 / agent-tools T3

    Already in catalog as the foundational MemGPT paper. Note: Letta is the productionised successor (cross-listed).

  • Transformer-XL T3

    Extends context through segment-level recurrence + caching of hidden states from prior segments. Foundational long-context architecture.

  • Generative Agents T3

    Park et al. — landmark agent-simulation paper. Reflection + memory stream + retrieval enable believable agent behavior.

  • MemoryBank T3

    Enhances LLMs with long-term memory. Early influential paper.

  • Reflexion T3

    Language agents with verbal reinforcement learning.

Sources (22 cited facts, 0 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).