[Paper] Grounding Agent Memory in Contextual Intent

Source: arXiv - 2601.10702v1

Overview

Deploying large language models (LLMs) as autonomous agents in multi‑step, goal‑driven tasks is still brittle: the same entities and facts keep popping up under different hidden goals, and the agent’s memory often pulls in the wrong piece of context. The paper “Grounding Agent Memory in Contextual Intent” introduces STITCH (Structured Intent Tracking in Contextual History), a memory‑indexing framework that tags each interaction step with a compact “intent” signal, enabling the agent to retrieve only the most relevant past experiences. The authors also release CAME‑Bench, a new benchmark for testing context‑aware retrieval in realistic, dynamic trajectories.

Key Contributions

• STITCH memory system: a lightweight indexing scheme that couples every dialogue/trajectory step with a three‑part contextual intent (latent goal, action type, salient entity types).
• Intent‑driven retrieval: at inference time, memory snippets are filtered and re‑ranked based on how well their intents match the current step, dramatically cutting down on “distractor” evidence.
• CAME‑Bench: a benchmark of long‑horizon, goal‑oriented interaction sequences that stresses context‑sensitive retrieval, complementing existing suites like LongMemEval.
• State‑of‑the‑art results: STITCH outperforms the strongest baseline by 35.6 % on average, with the gap widening as trajectory length grows.
• Comprehensive analysis: ablations show that each component of the intent signal (goal, action, entity) contributes to noise reduction and reasoning stability.

Methodology

1. Trajectory Segmentation – The interaction log is split into steps (e.g., “ask user for location”, “fetch weather”).
2. Intent Extraction – For each step, three signals are extracted:
   • Latent Goal – the high‑level objective the step serves (e.g., plan trip, diagnose issue).
   • Action Type – the kind of operation performed (query, compute, respond).
   • Entity Types – the categories of entities that matter in that step (location, date, device).
     These signals are encoded as a short vector or token tag.
3. Memory Indexing – The step’s full text plus its intent tag are stored in a searchable index (e.g., FAISS or Elastic).
4. Intent‑Aware Retrieval – When the agent needs to recall prior context, it first matches the current intent against stored intents, filters out low‑compatibility entries, and then runs a semantic similarity search on the remaining subset.
5. Evaluation – The authors test on CAME‑Bench and LongMemEval, measuring retrieval precision/recall and downstream task success (e.g., correct plan generation).

Results & Findings

• Retrieval Accuracy: STITCH achieves ~90 % top‑k precision on CAME‑Bench, versus ~65 % for the best prior method.
• Task Success: In long‑horizon planning tasks, agents using STITCH complete 42 % more goals correctly than baseline agents.
• Scalability: Performance gains increase with trajectory length; on sequences >100 steps, STITCH’s advantage climbs to >45 % over baselines.
• Ablation Insights: Removing any intent component (goal, action, or entity) drops performance by 8‑12 %, confirming that the three‑part signal jointly disambiguates context.

Practical Implications

• More Reliable AI Assistants – Voice assistants, customer‑support bots, or code‑generation agents can maintain coherent state over long conversations without “forgetting” or mixing up similar entities.
• Reduced Compute Costs – By pruning the retrieval pool early with intent filters, STITCH cuts down the number of expensive embedding similarity calculations, leading to faster response times.
• Plug‑and‑Play Integration – STITCH is model‑agnostic; it can sit on top of any LLM (GPT‑4, Claude, LLaMA) and any existing vector store, making it easy to retrofit into existing pipelines.
• Better Debugging & Auditing – The explicit intent tags provide a human‑readable trace of why a particular memory snippet was selected, aiding compliance and troubleshooting.

Limitations & Future Work

• Intent Extraction Reliance – The current pipeline assumes a reasonably accurate classifier for latent goals and entity types; noisy intent tags can degrade retrieval.
• Domain Generalization – Benchmarks focus on synthetic or semi‑structured tasks; real‑world domains with highly ambiguous intents (e.g., open‑ended creative writing) may need richer intent representations.
• Scalability to Massive Histories – While intent filtering reduces the candidate set, indexing billions of steps still poses storage and latency challenges.

Future directions include learning intent representations end‑to‑end with the LLM, extending the framework to multimodal memories (images, code snippets), and exploring hierarchical intent structures for ultra‑long‑term planning.

Authors

• Ruozhen Yang
• Yucheng Jiang
• Yueqi Jiang
• Priyanka Kargupta
• Yunyi Zhang
• Jiawei Han

Paper Information

• arXiv ID: 2601.10702v1
• Categories: cs.CL, cs.AI, cs.IR
• Published: January 15, 2026
• PDF: Download PDF