Pith Number

pith:5OVVRCK3

pith:2026:5OVVRCK3QAVKBZI6OERYJRBOVH

not attested not anchored not stored refs resolved

Learning to Learn from Multimodal Experience

Bing Qin, Dandan Tu, Weixiang Zhao, Xingyu Sui, Yang Wu, Yanyan Zhao, Yongxin Tang

Agents can substantially improve their performance on multimodal tasks by learning to dynamically construct and use memory from their experiences rather than depending on fixed designs.

arxiv:2605.16857 v1 · 2026-05-16 · cs.AI

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

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{5OVVRCK3QAVKBZI6OERYJRBOVH}

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

adaptive memory design substantially enhances agent performance and generalization across multimodal tasks

C2weakest assumption

the optimal way to structure and utilize multimodal experience is highly task-dependent and evolves over time, rendering fixed memory designs insufficient

C3one line summary

Agents learn to dynamically construct and organize memory from multimodal experiences, improving performance over static designs in task-dependent settings.

References

65 extracted · 65 resolved · 0 Pith anchors

[1] text“ (“str“, optional): guidance for the execution agent. - “images“ (optional): list of image refs with the same JSON shape as trajectory “ImageRef“: “{

[2] **examples** - **examples**: sampled retrieve trajectories, split into **FAILED TRAJECTORIES** and **SUCCESSFUL TRAJECTORIES**. - In each trajectory section below, every episode may include a text blo

[3] Need to use the score to analyze the performance and bottleneck of current memory structure

[4] Look at the provided improve_score (positive → improvement, negative → degradation) and the single suggestion_example that produced that score

[5] Step 2 — Inspect sampled trajectories and benchmark performance and decide which memories are useful

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:03:26.571243Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ebab58895b802aa0e51e712384c42ea9e1cea3f8ed467c73aad54eb7aa66cabf

Aliases

arxiv: 2605.16857 · arxiv_version: 2605.16857v1 · doi: 10.48550/arxiv.2605.16857 · pith_short_12: 5OVVRCK3QAVK · pith_short_16: 5OVVRCK3QAVKBZI6 · pith_short_8: 5OVVRCK3

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/5OVVRCK3QAVKBZI6OERYJRBOVH \
  | 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: ebab58895b802aa0e51e712384c42ea9e1cea3f8ed467c73aad54eb7aa66cabf

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "382ab370292499827e39db0e9bec48cbe2f9635e8d4c452a1c677b52a8f58039",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-16T07:41:31Z",
    "title_canon_sha256": "3faf8d33f20e447ff83f46d9344b3ffead9f7bbd8baf6157d7d9f123318951b4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16857",
    "kind": "arxiv",
    "version": 1
  }
}