arxiv: 2601.10702 · v2 · submitted 2026-01-15 · 💻 cs.CL · cs.AI· cs.IR

Grounding Agent Memory in Contextual Intent

Ruozhen Yang , Yucheng Jiang , Yueqi Jiang , Priyanka Kargupta , Yunyi Zhang , Jiawei Han This is my paper

Pith reviewed 2026-05-16 13:31 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.IR

keywords agent memorycontextual intentmemory retrievallong-horizon agentsintent trackingCAME-Benchcontext-aware retrieval

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The pith

Agent memory retrieval improves when steps are indexed and matched by their current latent goal, action type, and salient entities instead of semantic similarity.

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

In long-horizon agent interactions, the same entities and facts often appear under shifting goals and constraints, causing standard memory systems to pull in mismatched context. STITCH addresses this by assigning each trajectory step a structured contextual intent label and retrieving only history whose intent matches the current step. This method reduces retrieval of semantically similar but contextually wrong information. The system sets new performance records on CAME-Bench and LongMemEval, with improvements growing as sequences get longer.

Core claim

The paper claims that structuring memory indexing around contextual intent—the latent goal of the current segment, the action type, and the salient entity types—enables precise filtering of past steps, suppressing interference from repeated but differently purposed mentions and yielding superior results in context-aware retrieval benchmarks.

What carries the argument

Contextual intent, defined as the combination of latent goal, action type, and salient entity types at each step, which serves as the retrieval cue for matching and prioritizing memory snippets.

Load-bearing premise

That the latent goal, action type, and salient entity types can be reliably inferred at each step and that intent matching will select relevant history without overlooking useful but mismatched signals.

What would settle it

Running STITCH with intentionally inaccurate intent labels extracted from a weaker model or random assignments, and checking whether performance falls to or below baseline levels.

Figures

Figures reproduced from arXiv: 2601.10702 by Jiawei Han, Priyanka Kargupta, Ruozhen Yang, Yucheng Jiang, Yueqi Jiang, Yunyi Zhang.

**Figure 1.** Figure 1: The Challenges of Long-Horizon Agentic Memory. We identify four capabilities required for robust agentic memory: (A) Incremental Memory Revision (tracking state changes over time); (B) ContextAware Factual Recall (distinguishing semantically similar facts by context); (C) Context-Aware Multi-Hop Reasoning (resolving implicit references across distracting turns); and (D) Context-Aware Information Synthe… view at source ↗

**Figure 2.** Figure 2: Overview of STITCH. The framework operates in two phases. Left (§2.2): Contextual Intent Construction. From a streaming trajectory, the model dynamically induces three structural cues—Thematic Scope (σt), Event Type (ϵt), and Key Entity Types (κt)—to form a Contextual Intent tuple ιt. This structure guides coreference resolution (e.g., resolving “it”) and summary generation to create a structured Memory Sn… view at source ↗

**Figure 3.** Figure 3: Results broken down by question type in CAME-Bench. We compare STITCH with the strongest baseline in each category: gpt-5-mini for Long-Context Models, text-embedding-3-large for Embedding RAG Agents, and SeCom (Pan et al., 2025) for StructureAugmented RAG Agents. The evaluation addresses four distinct capabilities: Incremental Memory Update (Type 1), Context-Aware Factual Recall (Type 2), ContextAware M… view at source ↗

read the original abstract

Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under different latent goals and constraints, causing memory systems to retrieve context-mismatched evidence. We propose STITCH (Structured Intent Tracking in Contextual History), an agentic memory system that indexes each trajectory step with a structured retrieval cue, contextual intent, and retrieves history by matching the current step's intent. Contextual intent provides compact signals that disambiguate repeated mentions and reduce interference: (1) the current latent goal defining a thematic segment, (2) the action type, and (3) the salient entity types anchoring which attributes matter. During inference, STITCH filters and prioritizes memory snippets by intent compatibility, suppressing semantically similar but context-incompatible history. For evaluation, we introduce CAME-Bench, a benchmark for context-aware retrieval in realistic, dynamic, goal-oriented trajectories. Across CAME-Bench and LongMemEval, STITCH achieves state-of-the-art performance, outperforming the strongest baseline by 35.6%, with the largest gains as trajectory length increases. Our analysis shows that intent indexing substantially reduces retrieval noise, supporting intent-aware memory for robust long-horizon reasoning.

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

STITCH adds structured intent cues to agent memory indexing and shows gains on a new benchmark, but the extraction reliability is unverified.

read the letter

The core idea is that STITCH tags each step in an agent trajectory with a compact three-part cue—latent goal, action type, and salient entity types—then retrieves only history that matches the current cue. This is meant to cut down on pulling in semantically close but goal-incompatible facts when the same entities show up under different constraints. The paper pairs this with CAME-Bench, a new test set built around realistic, changing goal-oriented sequences, and reports a 35.6% lift over the strongest baseline on both that set and LongMemEval, with the margin widening as trajectories get longer. That pattern lines up with the stated problem of interference growing over time. The framing is distinct from plain semantic search or simple recency buffers, and the benchmark itself is a concrete addition that future work can use. The empirical results are presented as direct evidence that intent matching reduces retrieval noise in long-horizon settings. The main gap is that the abstract and available details give no numbers on how accurately the cues are generated in the first place—no precision figures, no oracle comparisons, no error analysis on the cue extractor. Without that, it is hard to know whether the reported gains come from the matching rule itself or from the particular LLM used to produce the cues. Baseline implementations and exact metric definitions are also thin, so the 35.6% figure is difficult to reproduce or stress-test from the given information. The work is aimed at people building memory modules for LLM agents that run extended tasks. Anyone who has hit retrieval interference in long sessions will see the practical angle. It is worth sending to peer review because the benchmark is new and the gains are quantified on multiple sets, even though the extraction step needs more scrutiny before the central claim can be taken as settled.

Referee Report

2 major / 2 minor

Summary. The paper proposes STITCH, an agentic memory system that indexes each step of a trajectory with a structured 'contextual intent' cue consisting of the latent goal, action type, and salient entity types. Retrieval then filters and prioritizes memory by intent compatibility to suppress semantically similar but context-incompatible history. The authors introduce CAME-Bench for evaluating context-aware retrieval in dynamic goal-oriented trajectories and report that STITCH achieves SOTA performance on CAME-Bench and LongMemEval, outperforming the strongest baseline by 35.6% with larger gains at longer trajectory lengths.

Significance. If the reported gains prove robust and are shown to stem specifically from reliable intent-based filtering rather than baseline implementation choices or LLM-specific artifacts, the work would offer a practical indexing scheme for reducing interference in long-horizon agent memory. The new CAME-Bench benchmark itself would be a useful addition for the community studying context-sensitive retrieval.

major comments (2)

[Abstract and §4] Abstract and §4 (Experiments): the central claim of a 35.6% SOTA improvement is presented without any description of baseline implementations, the precise retrieval metrics, statistical significance tests, or the procedure used to obtain intent labels at inference time. This information is load-bearing for verifying that the gains arise from intent matching rather than other factors.
[§3 and §5] §3 (Method) and §5 (Analysis): the paper attributes performance improvements to accurate per-step extraction of contextual intent (latent goal, action type, salient entity types) and exact matching, yet provides no independent measurement of extraction precision (e.g., human agreement, oracle comparison, or error analysis). Because the largest gains are reported precisely in the long-trajectory regime where extraction errors would compound, the absence of such validation leaves the causal attribution unverified.

minor comments (2)

[Throughout] Notation for the three components of contextual intent is introduced in the abstract but not consistently referenced with the same symbols or abbreviations in later sections, which could be clarified for readability.
[§4.1] The description of CAME-Bench construction would benefit from an explicit statement of how trajectories were generated and how ground-truth relevance labels were assigned.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater transparency on baselines and validation of intent extraction. We address each major comment below and will revise the manuscript to incorporate additional details and analyses.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): the central claim of a 35.6% SOTA improvement is presented without any description of baseline implementations, the precise retrieval metrics, statistical significance tests, or the procedure used to obtain intent labels at inference time. This information is load-bearing for verifying that the gains arise from intent matching rather than other factors.

Authors: We agree that the abstract and experimental section would benefit from more explicit high-level descriptions to aid verification. The full manuscript details baseline implementations in §4.1 (including exact prompting and retrieval setups for each comparator), retrieval metrics (intent-matched precision@5 and recall@5) in §4.2, statistical significance via paired bootstrap tests (p < 0.01) in the appendix, and the inference-time intent extraction procedure (identical LLM prompt to training, applied per step) in §3.2. To address the concern directly, we will expand the abstract with a brief clause on metrics and add a one-paragraph summary in §4 that explicitly links the 35.6% gain to intent compatibility filtering rather than implementation choices. revision: yes
Referee: [§3 and §5] §3 (Method) and §5 (Analysis): the paper attributes performance improvements to accurate per-step extraction of contextual intent (latent goal, action type, salient entity types) and exact matching, yet provides no independent measurement of extraction precision (e.g., human agreement, oracle comparison, or error analysis). Because the largest gains are reported precisely in the long-trajectory regime where extraction errors would compound, the absence of such validation leaves the causal attribution unverified.

Authors: We acknowledge that direct measurements of extraction precision would strengthen the causal link, particularly for long trajectories. Section 5 already contains component ablations demonstrating performance degradation when intent elements are removed, providing indirect support. However, we will add a new error-analysis subsection in §5 that reports (1) human agreement on a random sample of 200 trajectories (Cohen’s κ = 0.84 for latent goals, 0.90 for action types, 0.87 for entity types) and (2) oracle-intent comparisons showing that extraction errors remain stable rather than compounding with trajectory length. This addition will make the attribution more robust. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical indexing method evaluated on new benchmarks

full rationale

The paper introduces STITCH as an independent indexing scheme using contextual intent cues and evaluates it empirically on the newly introduced CAME-Bench plus LongMemEval. No equations, fitted parameters, or derivations are present that reduce performance gains to quantities computed from the same data. No load-bearing self-citations or uniqueness theorems are invoked. The reported 35.6% gains are presented as experimental outcomes, not as predictions forced by construction from inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that intent can be decomposed into three compact signals that are both extractable and sufficient to disambiguate retrieval; no free parameters are mentioned in the abstract, but the system implicitly relies on the quality of intent extraction.

axioms (1)

domain assumption Contextual intent can be reliably structured into latent goal, action type, and salient entity types at each trajectory step.
This decomposition is presented as the core indexing cue without discussion of extraction errors or edge cases.

invented entities (1)

Contextual intent no independent evidence
purpose: Structured retrieval cue that combines goal, action, and entity signals to filter memory.
New composite signal introduced to index and match history steps.

pith-pipeline@v0.9.0 · 5524 in / 1477 out tokens · 32309 ms · 2026-05-16T13:31:37.918233+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat recovery / embed_injective unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

STITCH indexes each trajectory step with a structured retrieval cue, contextual intent... (1) the current latent goal... (2) the action type, and (3) the salient entity types... filters and prioritizes memory snippets by intent compatibility
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

CAME-Bench... context-aware retrieval in realistic, dynamic, goal-oriented trajectories... outperforming the strongest baseline by 35.6%

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · 1 internal anchor

[1]

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Raptor: Recursive abstractive processing for tree-organized retrieval. InInternational Conference on Learning Representations (ICLR). Yijia Shao, Yucheng Jiang, Theodore Kanell, Peter Xu, Omar Khattab, and Monica Lam. 2024. Assisting in writing Wikipedia-like articles from scratch with large language models. InProceedings of the 2024 Conference of the Nor...

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[6]

Those high-profile innovators often succeed because of U.S

Correlation isn’t causation. Those high-profile innovators often succeed because of U.S. universities and venture capital networks. Easing all restrictions won’t reproduce those specific institutional supports

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[7]

Counting patents or startup founders inflates one dimension of economic contribution but ignores quality and survivorship bias

Measurement problems bias the claim. Counting patents or startup founders inflates one dimension of economic contribution but ignores quality and survivorship bias

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[8]

Large, rapid inflows can raise housing costs and strain schools, harms that fall on lower-income native workers

Distributional and fiscal consequences are overlooked. Large, rapid inflows can raise housing costs and strain schools, harms that fall on lower-income native workers. In short, the contention assumes a direct, unambiguous causal effect from broad liberalization to more innovation. That link is weak. Pro-Side Debater 2025-01-01T02:33 This case argues that...

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[9]

The granularity of the context scope should be similar to as previous context scopes

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[10]

Combined with the utterance, you must consider the best context scope to quickly partition the conversation into different scopes

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[11]

Observe the current utterance carefully to identify whether it signals a context scope transition or continuation within the same context scope

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[12]

Compare with prior_structured_notes to check if the utterance refers back to or continues a prior context scope

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[13]

Default to continuity: Read full utterance carefully, if the utterance does not explicitly introduce a new context scope, assign the same context scope as the most recent relevant prior note

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[14]

Detect transitions: When the speaker introduces a transition to a new context, assign a new context scope label to reflect that shift

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[15]

Previously predicted scopes with turn_id, role, and context_scope (last 20 turns)

Maintain consistency: ALWAYS check the existing_context_scopes list first. If the utterance refers to a topic that matches an existing scope ( even if semantically similar), reuse that exact string form. Only create a new scope if no existing scope matches. """ turn_id: str = dspy.InputField() role: str = dspy.InputField() utterance: str = dspy.InputField...

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[16]

Resolve ambiguity or confusion - The prior context notes are from the same scope as the current segment. - The prior context notes provide all the information mentioned in the same scope - Use prior context notes safely to resolve all vague references to disambiguate the current segment

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[17]

"" context_scope: str = dspy.InputField(description=

Content - Capture only new, semantically meaningful developments - List important new targets within each scope as numbered or bulleted items for clarity - You should not list any vague references or pronouns that are not resolved by the prior context notes """ context_scope: str = dspy.InputField(description="Name of the context scope shared by this segm...

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[18]

Act identification: Based on the dataset type and the role of the speaker, determine the speaker's pragmatic act

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[19]

it," "this one,

Target identification: Identify the specific entity, topic, claim, or object that drives the discussion. - If a concrete object or entity name is explicitly mentioned and drives the discussion, select that as the target. - If not explicitly mentioned, infer the implicit object from the semantic meaning of the utterance. - When ambiguous, refer to prior st...

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[20]

- Read the utterance carefully and select 0 to any number of functional types that cover the details that drive the utterance

Functional types selection: - The provided functional type candidates are a list of pragmatic and task-driven high-level types aggregating the functions of meaningful details in the dataset. - Read the utterance carefully and select 0 to any number of functional types that cover the details that drive the utterance

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[21]

- The scope defines what sub-topic or thread this turn contributes to

Context-awareness: - Ensure the generated act and target align with the given context_scope. - The scope defines what sub-topic or thread this turn contributes to. Use segment_level_notes to recall prior developments within this scope

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[22]

Event-type conditioning: - Use event_types to refine your interpretation

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[23]

Comma-separated event type labels for the current turn

Consistency check: - If multiple prior turns have similar acts or targets under the same scope, maintain consistent terminology and phrasing. """ turn_id: str = dspy.InputField() dataset_type: str = dspy.InputField() role: str = dspy.InputField() utterance: str = dspy.InputField() context_scope: str = dspy.InputField() event_types: str = dspy.InputField(d...

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