react-reasoning

name	react-reasoning
description	ReAct (Reasoning + Acting) framework — interleave chain-of-thought reasoning with tool actions to solve complex tasks. Eliminates hallucination by grounding every reasoning step in real observations. Use when the agent needs multi-step reasoning with external tool use: question answering, fact verification, web navigation, interactive decision making, or any task where pure reasoning hallucinates and pure acting fails to plan.
license	MIT
compatibility	Any Hermes Agent backend with tool access (web search, APIs, file system, or custom environments)
metadata	{"author":"hermeshub","version":"1.0","paper":"https://arxiv.org/abs/2210.03629","hermes":{"tags":["reasoning","acting","react","chain-of-thought","tool-use","grounding","multi-step","question-answering","fact-checking","decision-making","anti-hallucination"],"category":"research"}}

ReAct Reasoning

Synergize reasoning and acting in an interleaved loop. Based on the ReAct framework (Yao et al., ICLR 2023).

When to Use

• Multi-hop question answering that requires external knowledge retrieval
• Fact verification where claims must be checked against real sources
• Any task where chain-of-thought alone hallucinates facts
• Interactive decision making in complex environments (web navigation, file systems, APIs)
• Tasks requiring dynamic plan adjustment based on intermediate observations
• Situations where the agent needs to "think out loud" while taking actions

Core Concept

Human intelligence combines task-oriented actions with verbal reasoning (inner speech). ReAct replicates this by augmenting the agent's action space with a thought action — free-form language that does not affect the external environment but updates the agent's working context.

Standard action space:  A = {search, lookup, click, finish, ...}
ReAct action space:     Â = A ∪ L   (where L = language/thought space)

A thought (ˆa ∈ L) composes useful information by reasoning over the current context. It produces no external observation but updates the trajectory context for future steps.

The Thought-Action-Observation Loop

Every task is solved through an interleaved sequence:

Thought 1 → Action 1 → Observation 1 → Thought 2 → Action 2 → Observation 2 → ... → finish[answer]

Thought Types

Use thoughts for these purposes (mix freely):

Purpose	Example
Decompose goals	"I need to find X, then compare it with Y, then determine Z."
Extract from observations	"The paragraph says X was founded in 1844."
Commonsense reasoning	"X is not Y, so the answer must be Z instead."
Arithmetic reasoning	"1844 < 1989, so X came first."
Track progress	"I found X and Y. I still need Z."
Handle exceptions	"The search returned nothing useful. Let me reformulate as Q."
Synthesize answer	"Based on Obs 1-3, the answer is X because..."

Action Types

Actions interact with external tools and produce observations. Define actions appropriate to the task domain:

Knowledge retrieval (QA / fact-check):

• search[entity] — Search for an entity, return top results
• lookup[keyword] — Find next occurrence of keyword in current page
• finish[answer] — Submit final answer

Interactive environments (web, file system, APIs):

• Domain-specific actions (click, type, navigate, read, write, execute, etc.)
• Thoughts can appear sparsely — only at key decision points

Procedure

Step 1: Assess the Task Type

Determine which ReAct mode applies:

• Knowledge-intensive reasoning (QA, fact-check): Use dense thoughts — alternate Thought → Action → Observation at every step.
• Decision making (navigation, games, APIs): Use sparse thoughts — let reasoning appear only at critical decision points (goal decomposition, error recovery, plan changes). Actions can occur in sequences without intermediate thoughts.

Step 2: Decompose the Task

Before taking any action, produce an initial thought that:

1. Restates the goal in your own words
2. Identifies what information is needed
3. Outlines a tentative plan of actions

Example:

Thought 1: I need to find the birthplace of the director of "Inception" and
           the population of that city. First, I'll search for the director.

Step 3: Execute the Loop

For each step:

1. Think — Reason about the current state. What do you know? What do you still need? Did the last observation change your plan?
2. Act — Take the most informative action based on your reasoning.
3. Observe — Read the result. Extract key facts.
4. Repeat until you can confidently finish.

Step 4: Handle Failures

When an action returns unhelpful results:

Thought: The search for "X" returned no relevant results.
         Let me try searching for "Y" instead, which is an
         alternative name for the same concept.
Action:  search[Y]

Common recovery strategies:

• Reformulate the search query with synonyms or related terms
• Search for a broader topic and narrow down
• Try a different action type (lookup vs. search)
• Decompose a complex query into simpler sub-queries

Step 5: Synthesize and Finish

Before finishing, produce a synthesis thought:

Thought: Based on my observations: [Obs 1] told me X,
         [Obs 2] confirmed Y, and [Obs 3] showed Z.
         Combining these: the answer is W.
Action:  finish[W]

ReAct + CoT-SC Fallback Strategy

For maximum accuracy, combine ReAct with Chain-of-Thought Self-Consistency:

ReAct → CoT-SC fallback: When ReAct fails to return an answer within N steps (recommended: 5-7), fall back to CoT-SC (sample multiple chain-of-thought traces and take the majority answer).

CoT-SC → ReAct fallback: When the majority answer among CoT-SC samples has low agreement (appears less than half the time), fall back to ReAct to ground the reasoning in external evidence.

This hybrid approach captures the best of both: ReAct's grounded factuality and CoT's flexible reasoning structure.

Example: Multi-Hop Question Answering

Question: "What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?"

Thought 1: I need to search Colorado orogeny, find the area the eastern
           sector extends into, then find the elevation range of that area.
Action 1:  search[Colorado orogeny]
Obs 1:     The Colorado orogeny was an episode of mountain building...
           The eastern sector extends into the High Plains.

Thought 2: The eastern sector extends into the High Plains. I need to
           search High Plains and find its elevation range.
Action 2:  search[High Plains]
Obs 2:     High Plains refers to one of the two distinct land regions...
           elevation rises from around 1,800 to 7,000 ft.

Thought 3: High Plains rise from around 1,800 to 7,000 ft, so the
           elevation range is approximately 1,800 to 7,000 ft.
Action 3:  finish[approximately 1,800 to 7,000 ft]

Example: Fact Verification

Claim: "Nikolaj Coster-Waldau worked with the Fox Broadcasting Company."

Thought 1: I need to search Nikolaj Coster-Waldau and find if he
           worked with Fox Broadcasting Company.
Action 1:  search[Nikolaj Coster-Waldau]
Obs 1:     Nikolaj William Coster-Waldau is a Danish actor...
           He starred in Fox television series New Amsterdam (2008).

Thought 2: He appeared in "New Amsterdam" which was a Fox series.
           This confirms the claim.
Action 2:  finish[SUPPORTS]

Example: Interactive Decision Making (ALFWorld)

Goal: Put a cool lettuce in the fridge.

> think: To solve this task, I need to find a lettuce, cool it with
         the fridge, and put it back in the fridge. First, I should
         check likely locations: countertop, fridge, cabinet.
> go to fridge 1
You see: The fridge is closed.
> open fridge 1
You see: a cup 1, a plate 2...
> think: No lettuce in the fridge. Let me check the countertop.
> go to countertop 1
You see: a bread 1, a lettuce 1, a saltshaker 2...
> take lettuce 1 from countertop 1
You pick up the lettuce 1.
> go to fridge 1
> cool lettuce 1 with fridge 1
You cool the lettuce 1 using the fridge 1.
> put lettuce 1 in/on fridge 1
You put lettuce 1 in the fridge 1. Task complete!

Key Performance Data

From the original paper (PaLM-540B):

Task	ReAct	CoT	Act Only	Best Combined
HotpotQA (EM)	27.4	29.4	25.7	35.1 (ReAct→CoT-SC)
FEVER (Acc)	60.9	56.3	58.9	64.6 (CoT-SC→ReAct)
ALFWorld (SR)	71%	—	45%	—
WebShop (SR)	40%	—	30.1%	—

Key findings:

• 0% hallucination in ReAct failure cases vs. 56% for CoT
• 94% true-positive rate for correct ReAct answers vs. 86% for CoT
• Finetuned ReAct (PaLM-8B) outperforms all PaLM-62B prompting methods
• GPT-3 with ReAct achieves 78.4% on ALFWorld

Pitfalls

• Search dependency: 23% of ReAct errors come from non-informative search results — always have reformulation strategies ready
• Structural rigidity: The Thought-Action-Observation format can reduce flexibility compared to free-form CoT — use sparse thoughts for action-heavy tasks
• Step limits: Set reasonable step limits (5-7 for QA, more for decision tasks) and fall back to CoT-SC if exceeded
• Overthinking: Not every action needs a preceding thought in decision-making tasks — only think at key decision points
• Retrieval quality: ReAct is only as good as the tools it can access — ensure search/lookup tools return relevant content

Verification

After generating a ReAct trajectory, verify:

• Every factual claim traces back to an observation (not invented)
• The final answer is supported by the chain of observations
• No observation was ignored or contradicted without explanation
• The trajectory would make sense to a human reader (interpretability check)

References

• Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR 2023. https://arxiv.org/abs/2210.03629
• Project page: https://react-lm.github.io/