Buffer of Thoughts (BoT)
What if an AI could learn from its own reasoning patterns and reuse them? Buffer of Thoughts maintains a library of distilled thought-templates — proven reasoning structures extracted from past successes — and retrieves the most relevant template when facing a new problem.
Introduced: Buffer of Thoughts was introduced in 2024 and selected as a NeurIPS 2024 Spotlight paper. The technique addresses a key limitation of existing reasoning methods: each problem is solved from scratch, even when similar reasoning patterns have worked before. BoT introduces a “meta-buffer” — a library of high-level thought-templates distilled from successful reasoning chains. When a new problem arrives, the system retrieves the most relevant template and instantiates it for the specific problem, achieving 11-51% improvements across reasoning benchmarks.
Modern LLM Status: BoT represents the frontier of meta-reasoning approaches. While most prompting techniques treat each query independently, BoT introduces the concept of reasoning memory — the idea that good reasoning patterns should be accumulated and reused. This aligns with how expert human problem-solvers work: they recognize problem types and apply known solution patterns. In production systems, BoT’s template library concept maps naturally to curated prompt libraries and retrieval-augmented reasoning pipelines.
Reuse Proven Reasoning Patterns
Most prompting techniques start fresh with each problem. Chain-of-Thought generates new reasoning steps every time. Tree of Thoughts builds new trees from scratch. This is like an expert who forgets everything they’ve learned between problems.
Buffer of Thoughts changes this by maintaining a “meta-buffer” — a collection of high-level thought-templates that capture proven reasoning strategies. When a new problem arrives, BoT: (1) identifies the problem type, (2) retrieves the most relevant thought-template from the buffer, (3) instantiates that template with problem-specific details, and (4) uses the instantiated template to guide reasoning.
Think of it like a master chef who doesn’t reinvent cooking from first principles for every dish — they draw on a library of proven techniques (sauté, braise, emulsify) and apply the right technique to the right ingredients.
Starting each reasoning chain from scratch wastes the patterns learned from previous problems. A thought-template captures the structural reasoning strategy (e.g., “decompose into sub-problems, solve each independently, check for contradictions, merge”) without the problem-specific details. This separation of strategy from content means the same reasoning pattern can be applied across many different problems, just as a mathematical proof technique works across many different theorems.
The Buffer of Thoughts Process
Five stages from buffer construction to template-guided reasoning
Build the Meta-Buffer
Accumulate thought-templates by distilling successful reasoning chains into high-level patterns. Each template captures the reasoning structure (decompose, compare, verify) without problem-specific content.
From solving 50 combinatorics problems, distill the template: “COMBINATORIAL_COUNTING: Identify independent choice dimensions → verify independence → apply multiplication principle → check edge cases (empty set, overcounting).”
Problem Recognition
When a new problem arrives, analyze its type and characteristics. Match it against the templates in the buffer to find the most relevant reasoning pattern.
New problem: “How many unique sandwiches can be made with 3 breads, 4 cheeses, 2 condiments?” → Pattern match: this is a combinatorial counting problem → retrieve COMBINATORIAL_COUNTING template.
Template Instantiation
Take the retrieved template and fill it with the specific details of the current problem. The template provides the reasoning scaffold; the problem provides the content.
Instantiate COMBINATORIAL_COUNTING: Dimensions = {bread: 3, cheese: 4, condiment: 2}. Independence check: bread choice doesn’t constrain cheese choice. Edge case: is “no condiment” an option?
Guided Reasoning
Execute the instantiated template, following its prescribed reasoning steps with the current problem’s data. The template ensures a proven reasoning strategy is applied consistently.
Following template steps: (1) Dimensions identified. (2) Independence verified. (3) Apply multiplication: 3 × 4 × 2 = 24. (4) Edge case check: if “no condiment” is valid, then 3 × 4 × 3 = 36. Final answer depends on problem constraints.
Buffer Update
After solving the problem, evaluate whether the reasoning chain revealed a new useful pattern. If so, distill it into a new template and add it to the buffer for future use.
The edge case about “no condiment” suggests a new template variant: COMBINATORIAL_WITH_OPTIONAL_DIMENSIONS — when some dimensions include a null/none option, add 1 to that dimension’s count before multiplying.
See the Difference
Why template-guided reasoning produces more reliable results
Standard CoT
Think step by step: If a store has 3 types of bread, 4 types of cheese, and 2 condiments, how many unique sandwiches can be made?
Step 1: Count bread options (3). Step 2: Count cheese options (4). Step 3: Count condiment options (2). Step 4: Multiply: 3 × 4 × 2 = 24 unique sandwiches.
Buffer of Thoughts
[Retrieved template: COMBINATORIAL_COUNTING — identify independent choice dimensions, confirm independence, apply multiplication principle, verify with edge cases] Apply this template: If a store has 3 types of bread, 4 types of cheese, and 2 condiments, how many unique sandwiches?
Dimensions: bread (3), cheese (4), condiment (2).
Independence check: Each choice is independent — bread doesn’t constrain cheese or condiment selection. Confirmed.
Multiplication principle: 3 × 4 × 2 = 24.
Edge cases: Is “no condiment” a valid option? If yes, condiment dimension becomes 3 (2 + none), giving 3 × 4 × 3 = 36. The problem states “2 condiments” without a “none” option, so 24 is the answer.
Final answer: 24 unique sandwiches.
Practice Responsible AI
Always verify AI-generated content before use. AI systems can produce confident but incorrect responses. When using AI professionally, transparent disclosure is both best practice and increasingly a legal requirement.
48 US states now require AI transparency in key areas. Critical thinking remains your strongest tool against misinformation.
Buffer of Thoughts in Action
See how thought-templates improve reasoning across domains
“Prove that the sum of two odd numbers is always even.”
Retrieved Template: PROOF_BY_CASES — (1) Express terms using formal definitions, (2) Perform algebraic manipulation, (3) Show result matches target form, (4) State conclusion.
Step 1 (Formal definitions): An odd number can be expressed as 2k + 1 where k is an integer. Let our two odd numbers be (2a + 1) and (2b + 1).
Step 2 (Algebraic manipulation): Sum = (2a + 1) + (2b + 1) = 2a + 2b + 2 = 2(a + b + 1).
Step 3 (Match target form): The result 2(a + b + 1) is of the form 2n where n = (a + b + 1), which is an integer. By definition, any number of the form 2n is even.
Step 4 (Conclusion): The sum of two odd numbers is always even. QED.
Template effectiveness: The PROOF_BY_CASES template ensured no steps were skipped. Always verify mathematical proofs independently before relying on them.
“Our API returns 200 OK but the response body is empty for certain users. It works for most users but fails silently for accounts created before 2023.”
Retrieved Template: SYSTEMATIC_DIAGNOSIS — (1) Isolate the differentiating factor, (2) Generate hypotheses ranked by likelihood, (3) Design minimal test for each, (4) Eliminate systematically, (5) Confirm root cause.
Step 1 (Isolating factor): Works for post-2023 accounts, fails for pre-2023. The differentiating factor is account creation date, suggesting a schema or data migration issue.
Step 2 (Hypotheses):
H1: Pre-2023 accounts use legacy schema missing a required field (most likely)
H2: A data migration left null values that cause serialization to return empty
H3: Permission/role system changed in 2023, old accounts have incompatible role format
Step 3 (Minimal tests):
H1: Query a pre-2023 account directly in DB, compare schema to post-2023
H2: Check for NULL vs empty string in the response-generating fields
H3: Compare role/permission format between old and new accounts
Step 4 (Elimination): H1 test reveals pre-2023 accounts lack “display_name” field added in the 2023 migration. The serializer returns empty body when this required field is null.
Step 5 (Root cause confirmed): Migration script added column but didn’t backfill existing records. Fix: backfill display_name for pre-2023 accounts. Verify this diagnosis against your actual codebase and database before implementing fixes.
“Two studies on remote work productivity reach opposite conclusions. Study A says remote workers are 13% more productive. Study B says they are 10% less productive. Analyze the discrepancy.”
Retrieved Template: COMPARE_AND_CONTRAST — (1) Identify key variables in each source, (2) Find methodological differences, (3) Identify confounding factors, (4) Synthesize a unified understanding, (5) State confidence and limitations.
Step 1 (Key variables): Study A measures output per hour. Study B measures project completion rates. Different productivity metrics yield different conclusions.
Step 2 (Methodological differences): Study A surveyed tech workers (individual contributors). Study B surveyed cross-functional teams requiring frequent collaboration. The populations differ fundamentally.
Step 3 (Confounding factors): Study A was conducted during pandemic lockdowns (forced remote). Study B was post-pandemic (voluntary remote). Selection effects differ dramatically.
Step 4 (Synthesis): Both studies are likely correct for their specific contexts. Remote work increases individual output per hour (fewer interruptions) but may decrease collaborative project throughput (coordination overhead). The discrepancy reflects different aspects of “productivity” rather than contradictory findings.
Step 5 (Confidence): Moderate confidence. Key limitation: neither study controls for job type and collaboration requirements simultaneously. This synthesis should be validated against the actual study methodologies and peer review status.
When to Use Buffer of Thoughts
Best for repeated problem types that benefit from accumulated reasoning patterns
Perfect For
When you solve similar problems regularly — debugging, code review, data analysis — templates capture what works and ensure consistent quality.
Systems that process thousands of queries can distill the best reasoning patterns into templates, improving performance over time without retraining.
Multi-step reasoning tasks where skipping a step leads to errors — templates enforce completeness and consistency across every attempt.
Organizations can codify their best problem-solving approaches as templates, making expert-level reasoning accessible to every team member.
Skip It When
If no existing template matches the problem type, BoT offers no advantage over standard reasoning — you need to solve it fresh first, then distill the pattern.
Straightforward lookups or one-step calculations don’t benefit from template overhead — the template retrieval and instantiation adds unnecessary complexity.
Creative writing, brainstorming, and open-ended exploration benefit from unconstrained thinking — templates can inadvertently limit creative output by imposing structure.
Use Cases
Where Buffer of Thoughts delivers the most value
Automated QA Systems
Maintain templates for common test patterns — boundary testing, regression checks, integration validation — ensuring consistent, thorough quality assurance across every release.
Tutoring Platforms
Build subject-specific reasoning templates that guide students through problem types — algebra word problems, physics derivations, essay analysis — with consistent pedagogical approaches.
Code Review Pipelines
Apply review templates that check for security vulnerabilities, performance issues, code style, and architectural consistency — the same expert-level review every time.
Research Analysis
Use literature review templates, methodology comparison templates, and statistical analysis templates to maintain rigor across large-scale research synthesis projects.
Customer Support Escalation
Template common escalation patterns — billing disputes, technical issues, account recovery — so every support interaction follows proven resolution paths.
Scientific Hypothesis Testing
Apply hypothesis-testing templates that enforce proper experimental design, control identification, statistical test selection, and result interpretation across research programs.
Where Buffer of Thoughts Fits
BoT bridges fresh reasoning and systematic template reuse
You don’t need a formal BoT system to benefit from this approach. Start by saving your best reasoning chains as reusable templates. When you solve a complex problem well, distill the reasoning pattern into a template: “First decompose, then verify each part, then check for contradictions.” Over time, your personal thought-template library becomes a powerful reasoning toolkit.
Related Techniques
Explore complementary reasoning techniques
Build Your Reasoning Library
Start creating reusable thought-templates or explore other advanced reasoning techniques.