Pith Number

pith:WIYSYBKL

pith:2025:WIYSYBKLT6KAFZIJ4NHDCDN4OX

not attested not anchored not stored refs pending

MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems

Changyue Wang, Jianming Long, Qingyao Ai, Weihang Su, Yichen Tang, Yiqun Liu

Existing benchmarks fall short for testing LLM memory and continual learning from user feedback.

arxiv:2510.17281 v7 · 2025-10-20 · cs.LG · cs.AI · cs.IR

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{WIYSYBKLT6KAFZIJ4NHDCDN4OX}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experiments show that the effectiveness and efficiency of state-of-the-art baselines are far from satisfying.

C2weakest assumption

The proposed user feedback simulation framework produces interactions that are representative of real user behavior in deployed LLM services.

C3one line summary

MemoryBench is a new multi-domain benchmark that simulates ongoing user feedback to evaluate continual learning in LLM systems, finding that state-of-the-art memory methods are ineffective and inefficient.

Cited by

24 papers in Pith

Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

MemTrace: Probing What Final Accuracy Misses in Long-Term Memory

Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments

Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline

Receipt and verification

First computed	2026-06-04T01:08:34.012262Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b2312c054b9f9402e509e34e310dbc75ebd8876649762a6849c4e66af433a12d

Aliases

arxiv: 2510.17281 · arxiv_version: 2510.17281v7 · doi: 10.48550/arxiv.2510.17281 · pith_short_12: WIYSYBKLT6KA · pith_short_16: WIYSYBKLT6KAFZIJ · pith_short_8: WIYSYBKL

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/WIYSYBKLT6KAFZIJ4NHDCDN4OX \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b2312c054b9f9402e509e34e310dbc75ebd8876649762a6849c4e66af433a12d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4ff4e8c264bfb56c67df2bf898eb63bd481ccea2ec76306bcf22b30426d5553e",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.IR"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-10-20T08:16:12Z",
    "title_canon_sha256": "095d3d1b025b1682a29930146308cf76f6ae061d3631eca8ec9c01f3c6840467"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2510.17281",
    "kind": "arxiv",
    "version": 7
  }
}