ARXIV:2603.04885 · MEMORY SYSTEMS FOR STREAMING DIALOGUES · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

arXiv

ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications. It thus requires bounded-state memory mechanisms to operate within an infinite horizon.

METHOD

Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon.

Full abstract

Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. However, existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc memory recall while streams unfold. To explore this challenge, we introduce \textbf{STEM-Bench}, the first benchmark for \textbf{ST}reaming \textbf{E}valuation of \textbf{M}emory. It comprises over 14K QA pairs in dialogue streams that assess perception fidelity, temporal reasoning, and global awareness under infinite-horizon constraints. The preliminary analysis on STEM-Bench indicates a critical \textit{fidelity-efficiency dilemma}: retrieval-based methods use fragment context, while full-context models incur unbounded latency. To resolve this, we propose \textbf{ProStream}, a proactive hierarchical memory framework for streaming dialogues. It enables ad-hoc memory recall on demand by reasoning over continuous streams with multi-granular distillation. Moreover, it employs Adaptive Spatiotemporal Optimization to dynamically optimize retention based on expected utility. It enables a bounded knowledge state for lower inference latency without sacrificing reasoning fidelity. Experiments show that ProStream outperforms baselines in both accuracy and efficiency.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. However, existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc memory recall while streams unfold.

WHY NOW

Memory Systems for Streaming Dialogues moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

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

ARXIV:2603.04885 · MEMORY SYSTEMS FOR STREAMING DIALOGUES · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

arXiv

ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon.

Full abstract

RESULT

WHY NOW

Memory Systems for Streaming Dialogues moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

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

Paper Pack

10.48550/arXiv.2603.04885

Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

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 materialized8 extracted42 indexednot indexed

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

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon.

RESULT

WHY NOW

Memory Systems for Streaming Dialogues moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we introduce STEM-Bench, the first benchmark for STreaming Evaluation of Memory
Implicationpartial
Explicitly stated in abstract with clear description of benchmark scope and novelty
Verificationpartial
partial
Evidencepartial
It comprises over 14K QA pairs in dialogue streams that assess perception fidelity, temporal reasoning, and global awareness
Implicationpartial
Direct numeric claim about benchmark size with specific assessment dimensions listed
Verificationpartial
partial
Evidencepartial
existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc memory recall while streams unfold
Implicationpartial
Direct statement about limitation of existing approaches with clear reasoning
Verificationpartial
partial
Evidencepartial
The preliminary analysis on STEM-Bench indicates a critical fidelity-efficiency dilemma: retrieval-based methods use fragment context, while full-context models incur unbounded latency
Implicationpartial
Directly stated as a key finding from preliminary analysis with clear trade-off description
Verificationpartial
partial
Evidencepartial
It enables ad-hoc memory recall on demand by reasoning over continuous streams with multi-granular distillation
Implicationpartial
Direct description of method's core capability with specific technical approach
Verificationpartial
partial
Evidencepartial
it employs Adaptive Spatiotemporal Optimization to dynamically optimize retention based on expected utility
Implicationpartial
Explicit technical description of a key component of the proposed method
Verificationpartial
partial
Evidencepartial
It enables a bounded knowledge state for lower inference latency without sacrificing reasoning fidelity
Implicationpartial
Direct claim about method's benefits with clear performance trade-off resolution
Verificationpartial
partial
Evidencepartial
Experiments show that ProStream outperforms baselines in both accuracy and efficiency
Implicationpartial
Direct performance claim but lacks specific numeric evidence in provided text
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

Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon.

Materials

PDF linked

Markets

Memory Systems for Streaming Dialogues

Competitors

not indexed

Competitive landscape

ProStream offers a proactive hierarchical memory system enabling efficient ad-hoc recall in streaming dialogues for real-time applications.

Segment

Memory Systems for Streaming Dialogues

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.04885 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

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Bluesky

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PDF

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References(42)

A Simple Yet Strong Baseline for Long-Term Conversational Memory of LLM Agents

2025Sizhe Zhou, Jiawei Han

Convomem Benchmark: Why Your First 150 Conversations Don't Need RAG

2025Egor Pakhomov, Erik Nijkamp et al.

LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning

2025Zhengjun Huang, Zhoujin Tian et al.

SGMem: Sentence Graph Memory for Long-Term Conversational Agents

2025Yaxiong Wu, Yongyue Zhang et al.

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents

2025Zhen Tan, Jun Yan et al.

From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

2025Bernal Jim'enez Guti'errez, Yiheng Shu et al.

A-MEM: Agentic Memory for LLM Agents

2025Wujiang Xu, Zujie Liang et al.

Towards Multi-Granularity Memory Association and Selection for Long-Term Conversational Agents

2025Derong Xu, Yi Wen et al.

Performance evaluations of large language models for customer service

2024Fei Li, Yanyan Wang et al.

LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

2024Di Wu, Hongwei Wang et al.

MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation

2024Hongjin Qian, Zheng Liu et al.

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

2024Bernal Jimenez Gutierrez, Yiheng Shu et al.

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

2024Darren Edge, Ha Trinh et al.

A Survey on the Memory Mechanism of Large Language Model-based Agents

2024Zeyu Zhang, Quanyu Dai et al.

RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

2024Chi-Min Chan, Chunpu Xu et al.

Evaluating Very Long-Term Conversational Memory of LLM Agents

2024Adyasha Maharana, Dong-Ho Lee et al.

Data Engineering for Scaling Language Models to 128K Context

2024Yao Fu, Rameswar Panda et al.

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

2024Jianlv Chen, Shitao Xiao et al.

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

2024Parth Sarthi, Salman Abdullah et al.

DialSim: A Real-Time Simulator for Evaluating Long-Term Dialogue Understanding of Conversational Agents

2024Jiho Kim, Woosog Chay et al.

Showing 20 of 42 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

none indexed

Extension

Builds On ThisStratMem-Bench: Evaluating Strategic Memory Use in Virtual Character Conversation Beyond Factual Recall

6.0

Builds On ThisHingeMem: Boundary Guided Long-Term Memory with Query Adaptive Retrieval for Scalable Dialogues

6.0

Builds On ThisThinking in Streaming Video

7.0

Builds On ThisSAM: State-Adaptive Memory for Long-Horizon Reasoning Agent

0.0

Builds On ThisSemantic-Aware Adaptive Visual Memory for Streaming Video Understanding

6.0

Builds On ThisAdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents

7.0

Builds On ThisFreshMem: Brain-Inspired Frequency-Space Hybrid Memory for Streaming Video Understanding

3.0

Builds On ThisLifeBench: A Benchmark for Long-Horizon Multi-Source Memory

5.0

Builds On ThisSee, Remember, Explore: A Benchmark and Baselines for Streaming Spatial Reasoning

7.0

Commercially relevant

Higher ViabilityThink While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models

9.0

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: 2603.04885
Route: /paper/bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues
Active tab: read
Artifact: bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues

Available agents

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Related-paper scout

API/MCP endpoints

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REST build passport API/api/v1/paper/bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues/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/bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
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

Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

Canonical ID bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues | Route /paper/bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues

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: Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

/buildability/bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues

Watchwatch

Subject: Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

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/bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues

Paper ref

bounded-state-in-an-infinite-horizon-proactive-hierarchical-memory-for-ad-hoc-recall-over-streaming-dialogues

arXiv id

2603.04885

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

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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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Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues

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