EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents

2026-06-04 · Yilong Li, Suman Banerjee, Tong Che arxiv

Long-horizon agents can archive large histories, but future answers still incur retrieval, rereading, and context costs. When retained memory misses answer-relevant evidence, the system must return to larger portions of the raw history. We study budgeted evidence survival: before the query is known, which source evidence should be retained so that it remains recoverable and usable under a fixed retained source-evidence token budget? We instantiate this setting as Budgeted Pre-Query Retention, where memory is written during ingestion and later read without access to the full raw stream. We introduce EMBER, a learned retention policy that constructs a compact, source-backed evidence state. EMBER stores evidence capsules: verbatim source excerpts paired with retrieval keys and update metadata, preserving both grounding and read-time access. Post-query outcome feedback trains the writer to preserve evidence across the ingestion-retrieval-answer chain. On LongMemEval-RR, our LongMemEval-derived retained-evidence protocol, EMBER-14B reaches 0.3017 F1 at the 8192-token retained-evidence comparison point, compared with 0.1765 for the strongest non-EMBER budgeted baseline. Across retained source-evidence budgets, EMBER improves F1, Retain-Recall, and Read-Recall, indicating that long-horizon memory depends on retaining evidence within the budget rather than rereading larger histories.

📄 PDF Abstract BibTeX arXiv:2606.05894

Code (0)

등록된 구현이 없습니다.

Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents

2026-06-09 · Qingcan Kang, Liu Mingyang, Shixiong Kai, Kaichao Liang 외 arxiv

Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts exceeding context windows, making memory retention a fundamental resource-allocation problem. Existing systems treat retention a…

Stochastic Optimization

Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency

2026-04-02 · Payal Fofadiya, Sunil Tiwari arxiv

Long-horizon conversational agents require persistent memory for coherent reasoning, yet uncontrolled accumulation causes temporal decay and false memory propagation. Benchmarks such as LOCOMO and LOCCO report performanc…

Learning What to Remember: Adaptive Probabilistic Memory Retention for Memory-Efficient Language Models

2025-10-09 · S M Rafiuddin, Muntaha Nujat Khan arxiv

Transformer attention scales quadratically with sequence length O(n^2), limiting long-context use. We propose Adaptive Retention, a probabilistic, layer-wise token selection mechanism that learns which representations to…

Document Summarization

What Should a Streaming Video Model Remember?

2026-06-15 · Haonan Ge, Yiwei Wang, Hang Wu, Yujun Cai arxiv

Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets. Existing methods address this by …

Answer Generation

Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data

2018-12-11 · ICLR 2019 5 · Hyunwoo Jung, Moonsu Han, Minki Kang, Sungju Hwang

Current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. One example of this is lifelong learning sc…

Lifelong learning Question Answering Scheduling