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ARXIV:2601.18771 · AI REASONING ENHANCEMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.18771AI REASONING ENHANCEMENTSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory.
Opportunity summary
Pain Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory. The field has evolved from traditional retrieval-augmented generation (RAG) frameworks to more sophisticated search-based frameworks that orchestrate multi-step reasoning through…
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external knowledge bases. The field has evolved from traditional retrieval-augmented generation…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external…
AI Reasoning Enhancement moved forward this cycle; last verified April 2026. Public score 5.0/10.
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Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory.
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Paper Pack
10.48550/arXiv.2601.18771Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory.
Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external knowledge bases. The field has evolved from traditional retrieval-augmented generation (RAG) frameworks to more sophisticated search-based frameworks that orchestrate multi-step reasoning through explicit search strategies. However, existing search frameworks still rely heavily on implicit natural language reasoning to determine search strategies and how to leverage retrieved information across reasoning steps. This reliance on implicit reasoning creates fundamental challenges for managing dependencies between sub-questions, efficiently reusing previously retrieved knowledge, and learning optimal search strategies through reinforcement learning. To address these limitations, we propose Dep-Search, a dependency-aware search framework that advances beyond existing search frameworks by integrating structured reasoning, retrieval, and persistent memory through GRPO. Dep-Search introduces explicit control mechanisms that enable the model to decompose questions with dependency relationships, retrieve information when needed, access previously stored knowledge from memory, and summarize long reasoning contexts into reusable memory entries. Through extensive experiments on seven diverse question answering datasets, we demonstrate that Dep-Search significantly enhances LLMs' ability to tackle complex multi-hop reasoning tasks, achieving substantial improvements over strong baselines across different model scales.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 5.0
PROBLEM
Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory. The field has evolved from traditional retrieval-augmented generation (RAG) frameworks to more sophisticated search-based frameworks that orchestrate multi-step reasonin...
METHOD
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external knowledge bases. The field has evolved from traditional retrieval-augmented genera...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external knowled...
WHY NOW
AI Reasoning Enhancement moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory. The field has evolved from traditional retrieval-augmented generation (RAG) frameworks to more sophisticated search-based frameworks that orchestrate multi-step reasoning through explicit search strategies.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external knowledge bases. The field has evolved from traditional retrieval-augmented generation (RAG) frameworks to more sophisticated search-based frameworks that orchestrate multi-step reasoning through explicit search strategies.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external knowledge bases.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Reasoning Enhancement moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Dep-Search offers a framework for enhancing LLM reasoning with dependency-aware search and persistent memory.
Segment
AI Reasoning Enhancement
Adoption evidence
No public code link in the paper record yet
Commercial read
5.0/10 public viability
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CITED BY
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reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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missing
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Regulatory load
missing
Current read
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Evidence
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Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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