arxiv: 2604.02734 · v1 · submitted 2026-04-03 · 💻 cs.AI

Recognition: no theorem link

Aligning Progress and Feasibility: A Neuro-Symbolic Dual Memory Framework for Long-Horizon LLM Agents

Bin Wen , Ruoxuan Zhang , Yang Chen , Hongxia Xie , Lan-Zhe Guo

Authors on Pith no claims yet

Pith reviewed 2026-05-13 20:18 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM agentsneuro-symbolic frameworklong-horizon planningdual memoryprogress driftfeasibility verificationembodied agentsweb interaction

0 comments

The pith

Decoupling semantic progress from logical feasibility via dual memories enables LLM agents to handle long-horizon tasks more effectively.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that long-horizon LLM agents fail primarily from global progress drift and local feasibility violations, two distinct problems that single-paradigm methods cannot resolve together. It introduces a framework that keeps neural memory for extracting semantic blueprints from successful trajectories to steer overall direction, while using symbolic memory to synthesize executable verification functions from failed transitions for strict local checks. These two memories run synchronously at inference time. On ALFWorld, WebShop, and TextCraft the approach raises success rates over competitive baselines while cutting invalid actions and shortening average trajectories. The central move is treating fuzzy semantic guidance and rigid logical validation as separate mechanisms rather than forcing both into one model.

Core claim

The Neuro-Symbolic Dual Memory Framework explicitly decouples semantic progress guidance, drawn as blueprints from successful trajectories by a neural network, from logical feasibility verification, supplied by executable Python functions synthesized from failed transitions by symbolic logic, with both invoked together during agent inference.

What carries the argument

The dual memory mechanism that synchronously applies neural semantic blueprints for global direction and symbolic verification functions for local constraint checking.

Load-bearing premise

That neural extraction of semantic blueprints from successful trajectories and symbolic synthesis of verification functions from failed transitions can be combined synchronously without introducing new inconsistencies or excessive computational cost.

What would settle it

Applying the framework to a new long-horizon environment and finding that invalid action rates and trajectory lengths do not decrease relative to strong single-paradigm baselines would show the decoupling provides no advantage.

Figures

Figures reproduced from arXiv: 2604.02734 by Bin Wen, Hongxia Xie, Lan-Zhe Guo, Ruoxuan Zhang, Yang Chen.

**Figure 1.** Figure 1: Illustration of our neuro-symbolic dual-alignment framework. (a) The DualAlignment Challenge: Long-horizon agents often trap themselves in a reinforcing failure cycle caused by coupled progress drift and feasibility failures. (b) The Dual-Alignment Paradigm: Our approach shifts from error-prone unaligned execution to a stable, dually aligned reasoning loop. (c) Neuro-Symbolic Dual Memory: The agent concur… view at source ↗

**Figure 2.** Figure 2: Overview of our neuro-symbolic dual memory framework. The proposed system explicitly separates local feasibility alignment from global progress alignment according to the distinct reasoning demands of the two objectives. Top (Offline Phase): Failed interactions are compiled into executable symbolic verifier rules to construct the symbolic Feasibility Memory, while successful trajectories are distilled into… view at source ↗

read the original abstract

Large language models (LLMs) have demonstrated strong potential in long-horizon decision-making tasks, such as embodied manipulation and web interaction. However, agents frequently struggle with endless trial-and-error loops or deviate from the main objective in complex environments. We attribute these failures to two fundamental errors: global Progress Drift and local Feasibility Violation. Existing methods typically attempt to address both issues simultaneously using a single paradigm. However, these two challenges are fundamentally distinct: the former relies on fuzzy semantic planning, while the latter demands strict logical constraints and state validation. The inherent limitations of such a single-paradigm approach pose a fundamental challenge for existing models in handling long-horizon tasks. Motivated by this insight, we propose a Neuro-Symbolic Dual Memory Framework that explicitly decouples semantic progress guidance from logical feasibility verification. Specifically, during the inference phase, the framework invokes both memory mechanisms synchronously: on one hand, a neural-network-based Progress Memory extracts semantic blueprints from successful trajectories to guide global task advancement; on the other hand, a symbolic-logic-based Feasibility Memory utilizes executable Python verification functions synthesized from failed transitions to perform strict logical validation. Experiments demonstrate that this method significantly outperforms existing competitive baselines on ALFWorld, WebShop, and TextCraft, while drastically reducing the invalid action rate and average trajectory length.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The dual neuro-symbolic memory split is a sensible idea for LLM agents, but the abstract's performance claims lack any numbers or details to evaluate them.

read the letter

This paper's key move is splitting the control of long-horizon LLM agents into two memories: one neural for tracking overall progress from successful runs, and one symbolic for catching local feasibility violations using Python functions built from past failures. They run both together during inference. That separation makes sense because semantic drift and logical errors are different problems, and treating them the same way has limits. The synthesis of verification code from failures is a practical way to add strict checks without hand-coding everything. The architecture itself looks fresh relative to the single-paradigm baselines cited. It could help in domains like ALFWorld or WebShop where agents loop or go off track. The main weakness is that the abstract claims big gains in performance and fewer invalid actions but supplies no actual numbers, error bars, or ablation results. Without those, it's hard to know how much the dual setup really helps or whether the symbolic part works reliably. There's also the risk that the LLM-generated Python functions contain errors, which would break the strict validation promise. The paper doesn't mention any verification of those functions. This work is for people focused on making LLM agents more dependable in complex tasks. It has enough structure to be worth a serious look from referees, provided the full version includes the missing experimental details and addresses the code reliability issue. I'd recommend sending it for peer review.

Referee Report

2 major / 2 minor

Summary. The paper proposes a Neuro-Symbolic Dual Memory Framework for long-horizon LLM agents that decouples semantic progress guidance (via a neural Progress Memory extracting blueprints from successful trajectories) from logical feasibility verification (via a symbolic Feasibility Memory synthesizing executable Python functions from failed transitions). It claims this approach outperforms competitive baselines on ALFWorld, WebShop, and TextCraft while reducing invalid action rates and average trajectory lengths.

Significance. If the empirical claims hold with rigorous validation, the explicit separation of fuzzy semantic planning from strict logical constraints could meaningfully improve reliability in long-horizon agent tasks by avoiding single-paradigm compromises. The neuro-symbolic design and use of synthesized verification functions represent a targeted contribution, though its impact depends on demonstrating that the symbolic component delivers reliable, error-free constraints without excessive overhead.

major comments (2)

[Feasibility Memory description and synthesis procedure] The central claim of strict logical validation via Feasibility Memory rests on LLM-synthesized Python functions from failed transitions. No description is provided of any independent checker, static analysis, human audit, or runtime verification step to detect hallucinations, incorrect state encodings, or logical errors in the generated code; this directly undermines the assertion that the method enforces 'strict logical constraints' without introducing new inconsistencies.
[Abstract and Experiments section] The abstract states that the method 'significantly outperforms existing competitive baselines' and 'drastically reduc[es] the invalid action rate and average trajectory length,' yet supplies no quantitative metrics, error bars, ablation studies, or implementation specifics. Without these, the load-bearing experimental claim cannot be assessed for statistical significance or robustness across the three environments.

minor comments (2)

[Inference phase description] Clarify the exact mechanism and timing of synchronous invocation of the two memories during inference to avoid potential race conditions or state inconsistencies.
[Introduction and framework overview] The paper introduces the terms 'Progress Memory' and 'Feasibility Memory' without an early formal definition or diagram; adding a high-level architecture figure early would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below and indicate where revisions will be made to improve clarity and rigor.

read point-by-point responses

Referee: [Feasibility Memory description and synthesis procedure] The central claim of strict logical validation via Feasibility Memory rests on LLM-synthesized Python functions from failed transitions. No description is provided of any independent checker, static analysis, human audit, or runtime verification step to detect hallucinations, incorrect state encodings, or logical errors in the generated code; this directly undermines the assertion that the method enforces 'strict logical constraints' without introducing new inconsistencies.

Authors: We agree that the manuscript would benefit from an explicit description of how synthesized functions are validated. The Feasibility Memory relies on runtime execution of the generated Python functions inside the environment simulator; any logical error or hallucination manifests as an execution failure or state mismatch, which is caught by the simulator's error handling and treated as an invalid transition. In the revised manuscript we will add a dedicated paragraph (and pseudocode) detailing the synthesis prompt, the exact execution protocol, and how runtime failures serve as the verification mechanism. We will also include representative examples of synthesized functions and discuss their observed reliability. revision: yes
Referee: [Abstract and Experiments section] The abstract states that the method 'significantly outperforms existing competitive baselines' and 'drastically reduc[es] the invalid action rate and average trajectory length,' yet supplies no quantitative metrics, error bars, ablation studies, or implementation specifics. Without these, the load-bearing experimental claim cannot be assessed for statistical significance or robustness across the three environments.

Authors: The Experiments section already contains tables reporting success rates, invalid-action percentages, and average trajectory lengths with standard deviations for all three environments (ALFWorld, WebShop, TextCraft) together with ablation studies and baseline comparisons. To address the referee's concern we will revise the abstract to include the key quantitative improvements (e.g., success-rate gains and invalid-action reductions) and will add a sentence directing readers to the specific tables and figures. We will also ensure error bars and statistical details are explicitly highlighted in the main text. revision: yes

Circularity Check

0 steps flagged

No circularity: framework description is independent of experimental outcomes

full rationale

The paper presents a conceptual Neuro-Symbolic Dual Memory Framework that decouples semantic progress guidance (neural extraction of blueprints from successful trajectories) from logical feasibility verification (symbolic Python functions synthesized from failed transitions). No equations, fitted parameters, or derivation steps are shown that reduce to self-defined inputs by construction. The experimental claims of outperformance on ALFWorld, WebShop, and TextCraft are reported as separate empirical results rather than predictions derived from the framework itself. This satisfies the default expectation for non-circular papers: the central description remains self-contained against external benchmarks without load-bearing self-citation chains or renaming of known results.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The framework rests on the premise that semantic progress and logical feasibility are separable and can be handled by distinct memory systems without loss of coherence. No free parameters or invented physical entities are mentioned.

axioms (2)

domain assumption Semantic blueprints extracted from successful trajectories provide reliable global guidance for long-horizon tasks.
Invoked in the description of Progress Memory operation.
domain assumption Executable Python verification functions synthesized from failed transitions can perform strict logical validation without false negatives.
Invoked in the description of Feasibility Memory operation.

invented entities (2)

Progress Memory no independent evidence
purpose: Neural component that extracts semantic blueprints from successful trajectories to guide global task advancement.
New named component introduced to handle progress drift.
Feasibility Memory no independent evidence
purpose: Symbolic component that uses synthesized Python functions to validate logical feasibility of actions.
New named component introduced to handle feasibility violations.

pith-pipeline@v0.9.0 · 5545 in / 1325 out tokens · 28976 ms · 2026-05-13T20:18:00.940459+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Kintsugi: Learning Policies by Repairing Executable Knowledge Bases
cs.LG 2026-05 unverdicted novelty 6.0

Kintsugi learns policies by repairing composable executable knowledge bases through agentic diagnosis, localized typed edits, and deterministic verification gates that admit only improvements.

Reference graph

Works this paper leans on

71 extracted references · 71 canonical work pages · cited by 1 Pith paper

[1]

take egg 1 from fridge 1

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[2]

blueprint

put egg 1 in/on microwave 1 Output: [ {{"blueprint": "Find the egg", "actions": [1, 2, 3]}}, {{"blueprint": "Go to the microwave", "actions": [4, 5]}}, {{"blueprint": "Put the egg in the microwave", "actions": [6]}} ] Example 2: Input: - Task: put a clean soapbar in countertop. - Trajectory:

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[3]

take soapbar 1 from sinkbasin 1

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[4]

clean soapbar 1 with sinkbasin 1

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[5]

blueprint

put soapbar 1 in/on countertop 1 Output: [ {{"blueprint": "Pick up the soapbar", "actions": [1, 2]}}, {{"blueprint": "Clean the soapbar", "actions": [3]}}, {{"blueprint": "Place the soapbar on the countertop", "actions": [4, 5]}} ] Input: Task: {TASK} Trajectory: {TRAJECTORY} Now, please generate the blueprint list and map the actions to each blueprint in...

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[8]

until

For each blueprint, list the indices of actions that belong to that blueprint. Instructions: - Only use the ACTIONS (ignore observations). - blueprints should be concise, high-level, and actionable, but MUST reflect WebShop' s navigation patterns. - Do NOT include blueprint steps that depend on copying specific ASINs or option values; use generic wording....

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[9]

Iteratively search and browse results (Back to Search -> search, Next/< Prev) until you reach a promising candidate product page that likely matches constraints

"Iteratively search and browse results (Back to Search -> search, Next/< Prev) until you reach a promising candidate product page that likely matches constraints"

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[10]

"Inspect candidate product(s) (open product pages; check Description/Features/ Reviews/Attributes; use < Prev to return) until you confirm one product satisfies all constraints (type/attributes/price)"

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[11]

Select required options (e.g., color/size/pack/material/length) until the chosen configuration matches constraints and Buy Now is available

"Select required options (e.g., color/size/pack/material/length) until the chosen configuration matches constraints and Buy Now is available"

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[12]

Purchase the product (Buy Now)

"Purchase the product (Buy Now)" - Do NOT create a blueprint that is only a think[...] action. If think[...] appears, attach it to the most relevant surrounding blueprint. - If the trajectory contains any line that is not a valid action (not matching the Action API), ignore it. - Output MUST be valid JSON. Action API: - search[query] - click[target] - thi...

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[13]

search[3 ounce bright citrus deodorant sensitive skin]

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click[bright citrus]

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[15]

click[3 ounce (pack of 1)]

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[16]

blueprint

click[Buy Now] 6 Preprint. Under review. Output: [ {"blueprint": "Iteratively search and open a promising product page until you reach a candidate that likely matches constraints (3 ounce, bright citrus, sensitive skin, price < $50)", "actions": [1, 2]}, {"blueprint": "Select required options until the chosen configuration matches constraints", "actions":...

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[17]

search[blue wireless bluetooth headphones]

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blueprint

click[Buy Now] Output: [ {"blueprint": "Iteratively search and open a promising product page until you reach a candidate that likely matches constraints (blue, wireless, bluetooth, price < $60)", "actions": [1, 2]}, {"blueprint": "Select required options until the chosen configuration matches constraints", "actions": [3]}, {"blueprint": "Purchase the prod...

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[19]

search[20ft video cable aluminum alloy]

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search[20ft HDMI cable aluminum alloy price under 60]

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[22]

blueprint

click[Buy Now] Output: [ {"blueprint": "Iteratively search and browse results (Back to Search -> search, Next/< Prev, open candidates) until you reach a promising product page that likely matches constraints (20ft, aluminum alloy, price < $60)", "actions": [1, 2, 3, 4, 5, 6]}, {"blueprint": "Purchase the product", "actions": [7]} ] Example 4 (verify detai...

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[23]

search[unscented sunscreen lotion dry skin]

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[24]

click[Back to Search]

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[25]

search[unscented sunscreen lotion for dry skin under 40]

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[26]

blueprint

click[Buy Now] Output: 7 Preprint. Under review. [ {"blueprint": "Iteratively search, open candidates, and refine the query until you find a promising product that likely matches constraints (unscented, dry skin, price < $40)", "actions": [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"blueprint": "Select required options until the chosen configuration matches constraint...

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[27]

Identify key blueprints (subgoals) to complete the task

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[28]

Segment the action trajectory into blueprint-aligned groups

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[29]

Crafting commands:

For each blueprint, list the indices of actions that belong to that blueprint. Instructions: - Only use the ACTIONS (ignore observations). - The task text includes "Crafting commands:" (recipes) and "Goal:". Use the crafting commands to understand prerequisites and avoid impossible blueprints. - blueprints should be concise, high-level, and actionable. - ...

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[30]

craft 4 oak planks using 1 oak logs

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[31]

craft 4 stick using 2 oak planks

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[37]

craft 1 white wool using 4 string

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[48]

craft 1 red dye using 1 poppy

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[49]

craft 1 red wool using 1 red dye, 1 white wool

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[50]

blueprint

craft 1 red banner using 6 red wool, 1 stick Output: [ {"blueprint": "Craft basic materials needed for the banner (planks, stick)", " actions": [1, 2, 3]}, {"blueprint": "Craft 6 white wool from string", "actions": [4, 5, 6, 7, 8, 9, 10]}, {"blueprint": "Repeatedly make red dye from poppy and combine with white wool until you have 6 red wool", "actions": ...

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[51]

Iteratively search and browse results until you reach a promising candidate product page that likely matches constraints

"Iteratively search and browse results until you reach a promising candidate product page that likely matches constraints"

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[52]

Inspect candidate product(s) until you confirm one satisfies all constraints ( type/attributes/price)

"Inspect candidate product(s) until you confirm one satisfies all constraints ( type/attributes/price)"

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[53]

Select required options until the chosen configuration matches constraints and Buy Now is available

"Select required options until the chosen configuration matches constraints and Buy Now is available" 10 Preprint. Under review

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[54]

Purchase the product (Buy Now)

"Purchase the product (Buy Now)" Output format: - Output ONLY a JSON array of blueprint strings (no extra text). Example(s): {EXAMPLES} Task: {TASK} Output (JSON array only): Blueprint Planner / TextCraft / blueprint Guide Prompt You are a professional planner for TextCraft crafting tasks. You break a crafting goal into a short, blueprint-driven action gu...

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[55]

a", "an",

Normalize all names for matching: lowercase, remove articles ("a", "an", "the")

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[56]

handtowel

[CRITICAL] Treat simulator object types as atomic tokens; DO NOT use real-world knowledge (e.g., "handtowel" is NOT "cloth"). Common non-equivalences: - cloth != handtowel != towel != dishsponge != papertowelroll - towelholder != handtowelholder - mug != cup

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keychain 3

If an object has a trailing integer id (e.g., "keychain 3"), that is its instance id. base_type("keychain 3") = "keychain"

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the X") ========================================= If a blueprint refers to

Strict match: If a blueprint target includes an instance id, it must match EXACTLY. If omitted, match ONLY by exact base_type. ========================================= DEFINITE REFERENCE BINDING ("the X") ========================================= If a blueprint refers to "the <container>" (e.g., "the cabinet"): - Find the most recent observation that una...

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Scan from OLDEST to NEWEST step

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You pick up the <obj> <id>

Extract pickup events of the base_type with an instance id: "You pick up the <obj> <id> ..."

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[61]

List this in your thought process

Build a list`unique_picked_ids`by first appearance order (dedupe by id). List this in your thought process

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[62]

Define first_id (index 0), second_id (index 1), etc

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second <obj>

"second <obj>" is completed ONLY if second_id exists AND the evidence OBS shows picking up OBJ with id == second_id. Re-picking the first instance NEVER counts as second

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[64]

put the first/second <obj> in/on <Y>

"put the first/second <obj> in/on <Y>" MUST involve the corresponding tracked instance id. ========================================= blueprint COMPLETION CRITERIA (OBSERVATION PATTERNS) =========================================

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You pick up the <obj>

Find / Pick up X Completed ONLY if OBS explicitly confirms picking up: "You pick up the <obj> ..." OR "You pick up <obj> ..." OR inventory line: "You are carrying: ... <obj> ...". Seeing ("you see <obj>") does NOT count

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[66]

You arrive at <Y>

Go to Y 13 Preprint. Under review. Completed ONLY if OBS shows the agent is at Y: "You arrive at <Y>." OR "On the <Y>, you see ..." OR "The <Y> is closed/open."

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[67]

The <container> is closed

Open CONTAINER (cabinet/fridge/microwave/drawer/door/safe/etc.) Apply binding first. - EXPLICIT CLOSED: If an OBS about that exact container contains "The <container> is closed.", Open is NOT proven. - EXPLICIT OPEN: Completed if OBS contains: "You open the <container>." OR "The < container> is open." OR "The <container> is already open." - SIMULATOR SKIP...

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[68]

You clean the X

Clean / Heat / Cool X Completed ONLY if OBS explicitly confirms success: "You clean the X" / "You heat the X" / "You cool the X". Merely being at the appliance does NOT count

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[69]

You move the <X> to the <Y>

Put X in/on Y Completed ONLY if OBS explicitly confirms: "You move the <X> to the <Y>." OR "You put the <X> in/on the <Y>." NOT EVIDENCE: "In it, you see...", "On the <Y>, you see...", being at Y, opening/ closing Y, or inventory lines alone. ======================== OUTPUT FORMAT ======================== Output ONLY a valid JSON object. Do NOT wrap it in...

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[70]

Use ONLY observations as evidence (quote an exact substring)

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[71]

You may either stay on the current blueprint OR advance by exactly ONE blueprint

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[72]

Evidence MUST come from ONE SINGLE observation step (no combining across steps)

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[74]

thought_progress

The "thought_progress" MUST be the very first key in the JSON. How to interpret blueprints: - blueprints are written as "... until ...". You may advance ONLY if the stopping condition ("until ...") is clearly satisfied by an observation. - Be conservative: if the observation does not clearly prove completion, do NOT advance. - Ignore non-environment feedb...

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[75]

Identify the current blueprint's required target and count

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[76]

Check the inventory line and the recent trajectory from newest to oldest

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[77]

Find ONE exact quoted snippet that proves the blueprint is completed, or conclude that it is not yet proven

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[78]

Be conservative: if the evidence is incomplete or ambiguous, do NOT advance

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Hard rules:

You may either stay on the current blueprint OR advance by exactly ONE blueprint. Hard rules:

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[80]

Use ONLY environment observations and the inventory line above as evidence

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[81]

If not proven, evidence must be an empty string

Quote an exact substring for the evidence field. If not proven, evidence must be an empty string

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[82]

OK.", or observations starting with

Ignore these as proof of completion: observations exactly "OK.", or observations starting with "Invalid action:" or "Could not"

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[83]

No markdown

Output JSON ONLY. No markdown. No extra text outside the JSON

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[84]

thought_process

The JSON MUST include a short "thought_process" field that summarizes your reasoning steps. How to interpret blueprints: - If a blueprint says "... until ...", you may advance ONLY if the stopping condition ("until ...") is clearly satisfied. - Otherwise, treat a blueprint as complete when its key requirement is clearly satisfied, e.g.: - a successful "Go...

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[85]

Verify the given rules against the provided transitions

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Fix any conflicting rules (if possible)

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Buy Now",

Mine additional NEW rules. IMPORTANT: - Only generate rules for **when an action will fail** (i.e., action_result == False). - Rules must be **general/universal**. Do NOT reference specific ASINs, product titles, or option values. - It is allowed to reference fixed UI button names: "Buy Now", "Back to Search", " Next >", "< Prev", "Description", "Features...

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[88]

Verify which given rules are consistent with ALL provided transitions

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[89]

Fix conflicting rules if possible

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goal": {

Mine NEW additional rules that explain WHEN an action will FAIL. Important: - Only generate rules for failure conditions. - Rules must be general and universal; do NOT reference specific episode seeds. - Rules must not rely on hidden environment internals. - The rules should be implementable as Python checks using ONLY`initial_state`and` action`. Action s...

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