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Paper Citation Record · LEDGER

StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.02810.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.02810 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:31:30.787415Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T08:24:26.953840Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0379451a-27f3-4bca-a956-ec490c5e5a5a · inbound

Scaling Laws for State Dynamics in Large Language Models cites this paper.

Scaling Laws for State Dynamics in Large Language Models StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T15:31:30.787415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:31:30.787415Z digest=sha256:f4d796503e497aba9583a804effc0a9ec7ddd150decc58e3bbd5d2de8677fe32

Observation 6aa3af0b-9d82-40a7-9d69-c5b52652698d · inbound

Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents cites this paper.

Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T12:16:25.216062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:16:25.216062Z digest=sha256:ca535c9b49e6b681856f5823c3b3f2710ce80ef6bdc540d8a37bc5062af3ca2b

Observation ba737db2-9860-4a93-be98-ec3157ced46c · inbound

Sari Sandbox: A Virtual Retail Store Environment for Embodied AI Agents cites this paper.

Sari Sandbox: A Virtual Retail Store Environment for Embodied AI Agents StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T10:12:28.945469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:12:28.945469Z digest=sha256:d3660e724bb7ac5217356af1c1064deb86a51e45c38ed4d241691109aaadc061

Observation 8bd2b9fd-210f-49af-8e92-aeef75a57fcc · inbound

The FIL Hypothesis: Inductive Biases Help with Kernel Engineering cites this paper.

The FIL Hypothesis: Inductive Biases Help with Kernel Engineering StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:24:26.955374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T06:06:37.832064Z digest=sha256:9640776f19a86f0e39b8f20599e10f77496eb8885531fc5ab6df753e4551c419

Observation 2e9fe24b-ebe1-4dc3-b239-d148b52f0319 · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

Reference 140

Resolution
unresolved
no resolver link, observed 2026-07-11T23:16:58.545731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T23:16:58.545731Z digest=sha256:87215e2d13257caa787917bebd72504af1ffb49c56ee21e97097c12162e85b07

Observation f99db9ec-d467-462e-ac55-0e78e4115b9d · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

Reference 140

Resolution
unresolved
no resolver link, observed 2026-07-13T07:02:13.140334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T07:02:13.140334Z digest=sha256:baa8206fd142ee0cae00686be2ef8e80d852eec61ed7de6d3bb3e95022f73516