ARXIV:2603.12056 · MULTIMODAL AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

arXiv

XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates. A central challenge is enabling such agents to continually improve without parameter updates by learning from…

METHOD

Full abstract

Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories. We identify two complementary forms of reusable knowledge essential for this goal: experiences, providing concise action-level guidance for tool selection and decision making, and skills, providing structured task-level guidance for planning and tool use. To this end, we propose XSkill, a dual-stream framework for continual learning from experience and skills in multimodal agents. XSkill grounds both knowledge extraction and retrieval in visual observations. During accumulation, XSkill distills and consolidates experiences and skills from multi-path rollouts via visually grounded summarization and cross-rollout critique. During inference, it retrieves and adapts this knowledge to the current visual context and feeds usage history back into accumulation to form a continual learning loop. Evaluated on five benchmarks across diverse domains with four backbone models, XSkill consistently and substantially outperforms both tool-only and learning-based baselines. Further analysis reveals that the two knowledge streams play complementary roles in influencing the reasoning behaviors of agents and show superior zero-shot generalization.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories.

WHY NOW

Multimodal Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainXSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.12056 · MULTIMODAL AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

arXiv

XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Multimodal Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainXSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.12056

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback35 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Multimodal Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Multimodal Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Multimodal Agents

Competitors

not indexed

Competitive landscape

XSkill is a dual-stream framework enabling multimodal agents to continually learn from experiences and skills without parameter updates.

Segment

Multimodal Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.12056 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(35)

AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios

2026Zhaochen Su, Jincheng Gao et al.

REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents

2026Zheng Chu, Xiao Wang et al.

Transductive Visual Programming: Evolving Tool Libraries from Experience for Spatial Reasoning

2025Shengguang Wu, Xiaohan Wang et al.

EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration

2025Runze Li, Yuwen Zhai et al.

Memory in the Age of AI Agents

2025Yuyang Hu, Shichun Liu et al.

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution

2025Zouying Cao, Jiaji Deng et al.

MemVerse: Multimodal Memory for Lifelong Learning Agents

2025Junming Liu, Yifei Sun et al.

Thinking with Programming Vision: Towards a Unified View for Thinking with Images

2025Zirun Guo, Minjie Hong et al.

Qwen3-VL Technical Report

2025Shuai Bai, Yuxuan Cai et al.

Octopus: Agentic Multimodal Reasoning with Six-Capability Orchestration

2025Yifu Guo, Zishan Xu et al.

FLEX: Continuous Agent Evolution via Forward Learning from Experience

2025Zhicheng Cai, Xinyuan Guo et al.

DeepEyesV2: Toward Agentic Multimodal Model

2025Jack Hong, Chenxiao Zhao et al.

TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning

2025Ming Li, Jike Zhong et al.

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle

2025Rong Wu, Xiaoman Wang et al.

Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoning

2025Xing-ming Guo, Utkarsh Tyagi et al.

ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

2025Siru Ouyang, Jun Yan et al.

MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents

2025Xijia Tao, Yihua Teng et al.

MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents

2025Shilong Li, Xingyuan Bu et al.

WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent

2025Xinyu Geng, Peng Xia et al.

PyVision: Agentic Vision with Dynamic Tooling

2025Shitian Zhao, Haoquan Zhang et al.

Showing 20 of 35 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkSKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

7.0

Prior WorkWebXSkill: Skill Learning for Autonomous Web Agents

7.0

Prior WorkEvoSkill: Automated Skill Discovery for Multi-Agent Systems

7.0

Extension

Builds On ThisCODESKILL: Learning Self-Evolving Skills for Coding Agents

0.0

Builds On ThisExperience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory

0.0

Builds On ThisFrom Context to Skills: Can Language Models Learn from Context Skillfully?

4.0

Builds On ThisDynamic Dual-Granularity Skill Bank for Agentic RL

4.0

Commercially relevant

Higher ViabilitySkillX: Automatically Constructing Skill Knowledge Bases for Agents

8.0

Higher ViabilityMMSkills: Towards Multimodal Skills for General Visual Agents

8.0

Higher ViabilityTrace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills

8.0

Conflicting

none indexed

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.12056
Route: /paper/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents
Active tab: read
Artifact: xskill-continual-learning-from-experience-and-skills-in-multimodal-agents

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents/paper-pack
REST build passport API/api/v1/paper/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

Canonical ID xskill-continual-learning-from-experience-and-skills-in-multimodal-agents | Route /paper/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents

MCP example

{
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  }
}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: XSkill: Continual Learning from Experience and Skills in Multimodal Agents

/buildability/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents

Watchwatch

Subject: XSkill: Continual Learning from Experience and Skills in Multimodal Agents

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/xskill-continual-learning-from-experience-and-skills-in-multimodal-agents

Paper ref

xskill-continual-learning-from-experience-and-skills-in-multimodal-agents

arXiv id

2603.12056

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

e8a8ed32d2ec8f802e6b4a21fceeaadf3131604240a7dddf6a214508796461af

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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XSkill: Continual Learning from Experience and Skills in Multimodal Agents

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

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Claim map

Constellation map

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References(35)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline