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Paper Citation Record · LEDGER

Automatic Prompt Optimization with "Gradient Descent" and Beam Search

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 69 inbound Pith citation observations for arXiv:2305.03495.

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

pith.paper-citation-record.v1
2305.03495 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 69 of 69 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:14:32.340920Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:40:03.102880Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c21e125a-3242-4b21-a107-19862b44f6e0 · inbound

Reflexion: Language Agents with Verbal Reinforcement Learning cites this paper.

Reflexion: Language Agents with Verbal Reinforcement Learning Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 21

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arxiv_id, observed 2026-05-10T13:51:41.963062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T13:51:41.915864Z digest=sha256:0913e99e3d3a9658e43b29264305447b6066d4e9d04d9610e8ebacdb661e84ea

Observation 248d197e-5664-46a0-b283-9a17060d7292 · inbound

Large Language Models as Optimizers cites this paper.

Large Language Models as Optimizers Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 28

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verified exact
arxiv_id, observed 2026-05-15T00:04:31.319892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T00:04:31.212102Z digest=sha256:e84ffe8561d642c5fc67c78741bebf10e00775f162ee8bf0026c578d156d9ce4

Observation becd4a9f-14a8-495a-99a4-ab0364ab9764 · inbound

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers cites this paper.

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 124

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arxiv_id, observed 2026-05-16T06:11:49.632758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-16T06:11:49.475825Z digest=sha256:d207cc33ff43d34d7c13a915af8a1fed1f0e460b6767dafac153215a93cdc95a

Observation 213284a3-377b-4f83-b988-8249236fa8cc · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 6

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arxiv_id, observed 2026-05-16T08:12:35.372889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:39eb3572166d42d064938995d7cf0b6c27fa30127cc1cd2b2aa648abed3b974d

Observation 48a488a9-4799-4f18-b2c6-34b41ca9b20b · inbound

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines cites this paper.

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 42

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verified exact
arxiv_id, observed 2026-05-11T18:57:46.984559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-11T18:57:46.756656Z digest=sha256:10ffc06882db294b2ecd8f0a006ca507ec8556ae5054ef79e1954986b3ec06dd

Observation 5ac2f298-efaf-462f-9ad2-c7d11eb7e95e · inbound

Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting cites this paper.

Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 53

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arxiv_id, observed 2026-05-17T01:59:51.390802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-17T01:59:51.326493Z digest=sha256:51fa5cbed58c3536a0fd956073cbecdf3995d17e647a99fd3ef995626a8997f1

Observation 42853ad6-8802-4dd5-a3bb-1204e374f4e0 · inbound

Keeping Experts in the Loop: Expert-Guided Optimization for Clinical Data Classification using Large Language Models cites this paper.

Keeping Experts in the Loop: Expert-Guided Optimization for Clinical Data Classification using Large Language Models Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T23:49:47.777789Z digest=sha256:d36f06c03ed48190dbd8a324656c57d3c3831123d458148212c5fa7bc5d9cb56

Observation ff0f7aca-7e9a-48b8-b17f-c48348e5c2f2 · inbound

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization cites this paper.

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:52:15.153246Z digest=sha256:8f85718866cd7fd990f75ae14f79cb517dfd1580a405ea35cc81462021fb136e

Observation 58ee331e-028c-4880-b473-3a0245e8a01d · inbound

GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers cites this paper.

GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 21

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no resolver link, observed 2026-08-11T16:52:28.858246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:52:28.858246Z digest=sha256:5849691f5dbc43c35f5703a1651c4c5ff95b4212a56b8effe43eb8edcc0400a6

Observation 15a976c4-3c5f-4b22-944a-7a56d05c7142 · inbound

Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models cites this paper.

Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 33

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no resolver link, observed 2026-08-11T12:08:03.446562Z

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source=arxiv_source observed=2026-08-11T12:08:03.446562Z digest=sha256:9e68d0769a16b234c21054452a078e3f144cffabb57225983263e6d9aeb344cd

Observation 6ec70e26-31ac-470f-af77-f1c385d5afd8 · inbound

Memory-Augmented Agent Training for Business Document Understanding cites this paper.

Memory-Augmented Agent Training for Business Document Understanding Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 28

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no resolver link, observed 2026-08-11T13:27:32.007712Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:27:32.007712Z digest=sha256:320be22e469d959cfc97444584efb5970026c1879e2adf06e3dd323ebf925226

Observation 370d08af-cab4-4270-8fc0-00d151c0797d · inbound

Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework cites this paper.

Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 25

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no resolver link, observed 2026-08-11T00:08:16.262491Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:08:16.262491Z digest=sha256:1374f142bb84b19ee091f97da37c45bf92f7131f5f13055e701869d805e0730d

Observation 25a67637-65b7-4a28-8045-d49684bf27b2 · inbound

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts cites this paper.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 8

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:14:06.503198Z digest=sha256:44686a376c545d99d575efc6c8bb0a4af3e0174f1b9acb6efc667df72970ab57

Observation 72977d4d-6982-49b8-b0ef-fe6e6f1df630 · inbound

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow cites this paper.

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 13

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no resolver link, observed 2026-08-10T11:33:24.646115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T11:33:24.646115Z digest=sha256:790ecf9715651ec8920a2c1325ae9a1860d1c888369d1e084ffdee9faca528d0

Observation ed5725ed-d274-40e8-89c1-761ee62b7939 · inbound

Generating Symbolic World Models via Test-time Scaling of Large Language Models cites this paper.

Generating Symbolic World Models via Test-time Scaling of Large Language Models Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 31

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no resolver link, observed 2026-08-08T21:45:45.383025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T21:45:45.383025Z digest=sha256:3774c7eb33f03da5460c46678d12eb4ebf1c9b94bffb7fc422a8f3d37285c1c1

Observation b0035b54-31fd-4826-8f7f-8f36abe1e12e · inbound

SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches cites this paper.

SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 54

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no resolver link, observed 2026-08-08T12:22:43.278203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T12:22:43.278203Z digest=sha256:4b16b791214cf8beed88d7c5edb37d2b573883d72f06f7e505cb3755f48c811c

Observation 9cb9fe6e-734b-47b7-ae38-2066156d5ec2 · inbound

MODP: Multi Objective Directional Prompting cites this paper.

MODP: Multi Objective Directional Prompting Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 2023

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no resolver link, observed 2026-08-16T10:14:32.340920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:14:32.340920Z digest=sha256:a5baf9f47f6c4bafb5d5f487fdba60c349500eae2687b4b07b721f4233df3538

Observation fcc4b4eb-1059-4a06-adf4-331c566e6781 · inbound

Local Prompt Optimization cites this paper.

Local Prompt Optimization Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 9

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no resolver link, observed 2026-08-16T05:35:33.343962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:35:33.343962Z digest=sha256:b435a2d31e2e15365b2978a1f9a42ea7211eb64bf277f1cb46b939e3125f9141

Observation dbae75c1-ee3e-4eb9-b514-2d082e80b1c6 · inbound

TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution cites this paper.

TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 59

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no resolver link, observed 2026-08-15T23:32:07.515621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:32:07.515621Z digest=sha256:a51f03471854accc5fac3d107ba03f9c2a641eea68fa9cb5a79b146bb9952091

Observation 06b76652-7bd8-4a69-9cb7-6fb1955cdbc1 · inbound

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow cites this paper.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 12

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no resolver link, observed 2026-08-15T22:00:35.734403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:00:35.734403Z digest=sha256:edeaa140dd89480271d314e68524e3bee9cf1ebfacbe03ef9ccd2d3ac13bb711

Observation 866b4b9a-3db6-4e01-aa06-335aa0eef24b · inbound

Monte Carlo Beam Search for Actor-Critic Reinforcement Learning in Continuous Control cites this paper.

Monte Carlo Beam Search for Actor-Critic Reinforcement Learning in Continuous Control Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:45:35.101965Z digest=sha256:bfb1648626ba18acbffba17c10f07ab81bff58c210aa33f496b48bc54ceec68d

Observation 68c6f579-4b73-4c96-a89c-7fa29cd3b437 · inbound

Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization cites this paper.

Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 25

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no resolver link, observed 2026-08-15T21:08:46.207594Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:08:46.207594Z digest=sha256:9c12ee76af2f01db7ef0b59f6ae5253509998fb0bbaab8ff61f5be9415047198

Observation ed60016a-60bb-4880-970b-71b09668513e · inbound

Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands cites this paper.

Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 19

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no resolver link, observed 2026-08-07T15:08:25.999511Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:08:25.999511Z digest=sha256:a7581acd11183ec15130c80c81a0846977e16b08e382b99c98943f14e583ff1f

Observation 67ae6db2-43e4-42a9-a8a1-91266ea4ffdc · inbound

Visual Large Language Models Exhibit Human-Level Cognitive Flexibility in the Wisconsin Card Sorting Test cites this paper.

Visual Large Language Models Exhibit Human-Level Cognitive Flexibility in the Wisconsin Card Sorting Test Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 38

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no resolver link, observed 2026-08-07T13:19:32.679972Z

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source=pdf_text observed=2026-08-07T13:19:32.679972Z digest=sha256:3ee18ca86ff0ab1a26790de7f5cbcd84ddd5d11333c44bddac934af70206aa52

Observation 03b6f9bb-dec4-4d97-89a5-6a47e344f3db · inbound

Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings cites this paper.

Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 44

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no resolver link, observed 2026-08-07T12:15:39.830669Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:15:39.830669Z digest=sha256:c96dfca57f24ee6a8023701dc93770a13149cbb3b8fec2258001483957431480

Observation 0dde4865-f67f-48c4-b329-bb5f47e9ba75 · inbound

Predicting Empirical AI Research Outcomes with Language Models cites this paper.

Predicting Empirical AI Research Outcomes with Language Models Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 12

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no resolver link, observed 2026-08-07T12:05:56.352320Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:05:56.352320Z digest=sha256:8c7cd6e49c84883418fa1094a9b396f6b2f5ee2ef238b75950af7ad121850ff3

Observation 80a63cd2-c49e-4284-80c9-33203b0a57ce · inbound

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization cites this paper.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 24

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no resolver link, observed 2026-08-07T13:24:34.774410Z

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source=pdf_text observed=2026-08-07T13:24:34.774410Z digest=sha256:c5fc7a7d6de6ff7715d5bd7bef560d2a9440b9b3de80f99e93c37bbaab23f58d

Observation c1c5b9b8-fa9c-4d5b-8bfe-23dae174d260 · inbound

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs cites this paper.

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 20

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:34.725290Z digest=sha256:fd66b8b06bc2b70ba37ef75e57e0b025e61a4e0d6059c1ae078b0bc4b689505d

Observation 9817da71-1d6d-4c95-bca6-6f241c3dd680 · inbound

Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search cites this paper.

Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 18

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:10:39.559056Z digest=sha256:8ff619c73a441294957d280c9529894836af06b7aebb2daffef53f3522f7b153

Observation 3f9b93ab-2e34-4361-8f83-873ae3112df1 · inbound

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future cites this paper.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 52

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no resolver link, observed 2026-08-15T19:07:06.877853Z

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source=pdf_text observed=2026-08-15T19:07:06.877853Z digest=sha256:cd77b0383ed525dc15d4eeeae4b4241b48bb47b6e6c5c9caf9d3c50b1254a7a7

Observation c60c9bbc-260c-499f-97d2-5f75df7fd49e · inbound

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models cites this paper.

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 31

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source=pdf_text observed=2026-08-06T20:21:23.373110Z digest=sha256:68c37b5de01bbe847c7360b16e5e0875e12eda4ed57c67b3a81d8ba229db119a

Observation 447b35a4-65a8-4979-8e55-6cbd5ae0b8db · inbound

Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing cites this paper.

Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 25

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no resolver link, observed 2026-08-06T18:33:05.012547Z

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source=arxiv_source observed=2026-08-06T18:33:05.012547Z digest=sha256:82a4e3c6d8ee4e5b6fcd72c114b27671ec9ecea27ebb1fcb229ea9a2afe4bbb5

Observation aadb03a5-8aad-4467-8735-bfb4c5b7b56f · inbound

Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation cites this paper.

Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 40

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no resolver link, observed 2026-08-06T17:51:03.278384Z

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source=pdf_text observed=2026-08-06T17:51:03.278384Z digest=sha256:f2bb269fe6b3ce730b5b986960490b920c6b2bede81a6b7cd57c6a3fbc8c0231

Observation 720098eb-df62-4700-8594-77f97dce52e5 · inbound

BuildEvo: Designing Building Energy Consumption Forecasting Heuristics via LLM-driven Evolution cites this paper.

BuildEvo: Designing Building Energy Consumption Forecasting Heuristics via LLM-driven Evolution Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 21

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no resolver link, observed 2026-08-06T16:56:11.700817Z

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source=arxiv_source observed=2026-08-06T16:56:11.700817Z digest=sha256:ae82d7930a5cee2495359a287699d122170b3fedcf0cd71bf060977ccc5b0eea

Observation 78ea5e32-c17c-4513-a199-c79c0c6fdb00 · inbound

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 67

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no resolver link, observed 2026-08-06T16:34:25.139637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:34:25.139637Z digest=sha256:d51571b7f03a308c40d441a2146343fa82455f23bfaf2106400670fde7b32986

Observation 840c83b5-d7dc-45b5-9c2a-6569e62b754a · inbound

Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications cites this paper.

Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 2023

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unresolved
no resolver link, observed 2026-08-06T16:11:25.974242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:11:25.974242Z digest=sha256:5a9c36140ae920884652af039a7bb4d2057635b4322366dae09193b28a569ded

Observation 35e7ddcc-6812-40fe-ac18-ea223bc4bb2e · inbound

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards cites this paper.

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T14:35:46.580669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:35:46.580669Z digest=sha256:b46db974492b81d466cabe32f45d949cb026e88b33c48c1270edbe265c66e2ff

Observation 3cddf09b-d4f8-4c9d-85b3-669bbd6404fa · inbound

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity cites this paper.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-15T22:37:38.443498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:37:38.443498Z digest=sha256:800d0587e84b2870179abc2b8bfab06958d47b35acbe20121264a44d8be6742b

Observation 2bdbb183-1cea-4b1c-b069-9490ddf2573b · inbound

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:23:15.590683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-14T22:23:14.621091Z digest=sha256:3511a67c2a10a6fd329911f83da68655dbd4694a3abed41b91f0449e26cd1752

Observation 515253c4-bd57-4470-8ce0-bd2e682d4bae · inbound

ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients" cites this paper.

ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients" Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T00:55:38.065300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:55:38.065300Z digest=sha256:9ada38e69e9881f6dfabe87ae1fef16a5cc5c7e9105dcbcaab27c177e8f38907

Observation 6e59c8d2-79bf-484e-8123-a79756763244 · inbound

CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series cites this paper.

CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T23:55:53.056029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T23:55:53.056029Z digest=sha256:160bf737d99c0a6802cb28e643b92249caf2cc065e6d477d68c9a04c58ac32b6

Observation 579e80bc-d222-41cb-9496-958a6861aefe · inbound

WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning cites this paper.

WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T17:15:24.202120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:15:24.202120Z digest=sha256:9be9c44a17b52a710dfbc1541b505964f6bd041c27992e35a964e90809ef3bd7

Observation 2ae57cb2-65ff-4ed8-9203-d33b666837aa · inbound

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback cites this paper.

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-04T15:47:12.941106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T15:47:12.941106Z digest=sha256:127442868b193de1837975362a87aef71feac327c6d1ceb625257a66d87efaf7

Observation 1b3e3c15-937a-493b-9528-1144b1466b80 · inbound

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level cites this paper.

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-03T20:29:15.134142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T20:29:15.134142Z digest=sha256:920ebdc16eaca78737fd456090ae15a14072b1c36f5a01aa920bcd20af20f5db

Observation a45e0833-4283-4a0e-b4ad-0791c01aac9d · inbound

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data cites this paper.

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:18:40.073746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-16T23:15:52.444217Z digest=sha256:d4e4a08a152bb1dfc2f6b456e7b303caa253ef4e7bd3a4bf19e1d9ec8b670c7c

Observation 51d39e98-5c40-4162-a6b7-aa2b89d2e52d · inbound

Visual Persuasion: What Influences Decisions of Vision-Language Models? cites this paper.

Visual Persuasion: What Influences Decisions of Vision-Language Models? Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 31

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unresolved
no resolver link, observed 2026-08-02T23:00:27.011059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T23:00:27.011059Z digest=sha256:8a1cc19944806b402c638e811f5ace851f584b498bfccf918ba8e4d4a562061c

Observation e988b4d7-58fc-499f-8bea-7ad2a6f5b85c · inbound

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search cites this paper.

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-15T17:50:12.234658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T17:49:46.383559Z digest=sha256:8c81950f27b9c300933a0b1e741300de3a2e6bdcb43fa835886b3be4495417ae

Observation 34d12499-f33d-4f66-9b19-971739a86db7 · inbound

MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks cites this paper.

MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-02T19:22:25.401817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T19:22:25.401817Z digest=sha256:b4963f9110f7026573243cb8ad9ac7bda73ee156d718b0c61cfc2ba5690ee8b3

Observation 7429ff8e-41b7-4603-b049-3e45aec47877 · inbound

CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training cites this paper.

CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-25T06:45:26.392459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-25T06:40:51.046965Z digest=sha256:f11527049d33ae54274a1130f37fd87be5dd22bad2f5edaaf3fad2109398a6bb

Observation fc9790a2-fa30-4e26-82b8-d49efe1a8f46 · inbound

Meta-Harness: End-to-End Optimization of Model Harnesses cites this paper.

Meta-Harness: End-to-End Optimization of Model Harnesses Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-13T16:15:58.020671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-13T16:15:57.877354Z digest=sha256:62c1e1dee3c82c86922083970e809b04a075143ad0b744aaf98922a9331cf779

Observation 7054d502-1b0e-4239-9319-1c9aacb3f196 · inbound

Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering cites this paper.

Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 120

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:20:59.723082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T17:40:14.733882Z digest=sha256:b2cdc1033d6366b5824db204b04b5bf7441ed2dece7793705639c1795df03f54

Observation 654bae84-0984-423c-84b7-af515b9411f0 · inbound

Prompt Optimization Is a Coin Flip: Diagnosing When It Helps in Compound AI Systems cites this paper.

Prompt Optimization Is a Coin Flip: Diagnosing When It Helps in Compound AI Systems Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:20:10.383973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T11:18:09.127345Z digest=sha256:e962ae88cf62cef96545bb3cc7589f3b5675cc7e5783b0ce9085d7806dab1638

Observation 90dd37db-fc11-464d-91bb-08891c753f77 · inbound

Embedding-perturbed Exploration Preference Optimization for Flow Models cites this paper.

Embedding-perturbed Exploration Preference Optimization for Flow Models Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-05-20T18:38:53.027822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-20T18:33:52.933672Z digest=sha256:7e3fdd8b0aeb5a3f79e715765d71576d415b51cd62fc8d05cb79349fae2d4fae

Observation 4c9b3fa0-77a0-4645-8abb-f281dd2f23ce · inbound

A Reproducibility Analysis of PO4ISR: Diagnosing and Mitigating Semantic Drift in LLM-Based Session Recommendation cites this paper.

A Reproducibility Analysis of PO4ISR: Diagnosing and Mitigating Semantic Drift in LLM-Based Session Recommendation Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-21T00:49:19.533621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-21T00:45:14.845401Z digest=sha256:98bebf5e97872151fa2f25a68c6d6248a25d63a7d540c68a335769d2669719e5

Observation f7bd9f60-3431-46c1-8a9c-a26c023ae623 · inbound

Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework cites this paper.

Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:17:29.499369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T18:21:57.096578Z digest=sha256:162361ab8efa58b4790b702026532e1ea0119b9b3211984806ba07a221814ad7

Observation a39b3b0b-5d08-4fb1-84a1-411370f9e410 · inbound

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience cites this paper.

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T13:48:21.407189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T07:28:32.185178Z digest=sha256:137100ecceacf49d8f280073359a28c5e5ad2aa9c911276b2133f629d6936a5b

Observation f4f18d23-9a3e-489c-bb8d-7cc519028ba9 · inbound

BCL: Bayesian In-Context Learning Framework for Information Extraction cites this paper.

BCL: Bayesian In-Context Learning Framework for Information Extraction Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:29:15.908307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-26T21:11:23.042546Z digest=sha256:6585ca31729b7d59b747279335080c463e15a80e2163f110f1ecb2249cf01b0d

Observation adb0eadf-fa52-4a37-a0e6-c8ed9cb663c6 · inbound

Marginal Advantage Accumulation for Memory-Driven Agent Self-Evolution cites this paper.

Marginal Advantage Accumulation for Memory-Driven Agent Self-Evolution Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T03:39:30.821536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-26T17:45:49.272070Z digest=sha256:ef7cc2aac0844464b4d555ba248869f0c9c49d04e1b88a898a9300b571aab677

Observation 061943ce-084a-435a-bdf3-68774a070a4f · inbound

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents cites this paper.

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:39:42.748336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-26T11:06:28.690956Z digest=sha256:ae5aaa36dc6f73635bc98f2b1461b7ed5130feae4b3bb6fc42216c863a7f186b

Observation 4fdfb860-2707-41d9-90b3-dcd53d517e61 · inbound

Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems cites this paper.

Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-07-04T12:09:48.813322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-26T07:13:21.198407Z digest=sha256:e7ef56c725d7672aa7bc33640c04a02323cf7be060df20644ef1298b5449df93

Observation 63fd08e3-f085-46b9-920e-49040285988e · inbound

LLM4MTLs: Automated Generation and Empirical Evaluation of Model Transformation Languages cites this paper.

LLM4MTLs: Automated Generation and Empirical Evaluation of Model Transformation Languages Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-07-04T18:40:03.104242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-25T22:38:37.806421Z digest=sha256:da1c076e2a1a647cf0d341f9111e73d27f6ad9dd5070236185f7fbede7bb45b0

Observation d26e4894-fb73-4ea1-afc8-99341156c044 · inbound

BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification cites this paper.

BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-07-02T09:16:48.666443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T09:12:59.039529Z digest=sha256:b7b01d5e32bd8a19ddcb5e643cac7aa70d96827059849e68b2a89d214d90b012

Observation faa636a4-b382-437e-bdca-723a4f9ea488 · inbound

Heuristic Learning for Active Flow Control Using Coding Agents cites this paper.

Heuristic Learning for Active Flow Control Using Coding Agents Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 25

Resolution
unresolved
no resolver link, observed 2026-07-14T04:44:42.371749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:d7b70c1da9c8e3f867e99ddb016100a94f4e32a6986ca6800c0ad1dbdb1654ad

Observation 5a966f4a-b337-4348-b2d5-2704c541d164 · inbound

MemoHarness: Agent Harnesses That Learn from Experience cites this paper.

MemoHarness: Agent Harnesses That Learn from Experience Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-02T05:43:00.443189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T05:43:00.443189Z digest=sha256:0982e121ff302eb1da106c1e56639417c7bcd09783b0c43845abf795a03401dd

Observation 3cffb090-0a89-462c-be0c-f4ea62d39747 · inbound

Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution cites this paper.

Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T00:20:37.207547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:20:37.207547Z digest=sha256:b1775662f1da8cb807db5fb23f3839d2c4e76dc68de300ca25bceaa35d2aa758

Observation cb3fabb6-421b-4749-a9a1-4ba47e3aa620 · inbound

Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories cites this paper.

Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-04T10:10:32.578208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:10:32.578208Z digest=sha256:f37e3ea77ba2be4891d79581146d5f2f87c197758331dc509f152fb7c2fe035d

Observation fe5e747e-cfbe-4cb1-86b1-f8cb6e2dcb3f · inbound

FLARE: Few-shot Learning-based Adaptive Reflective Engine cites this paper.

FLARE: Few-shot Learning-based Adaptive Reflective Engine Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-15T14:58:05.734796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:58:05.734796Z digest=sha256:409eb2978f1301a8431d824903518946c11352b8a1d86abfb21303e6a655b423

Observation dc143e12-766b-43b8-8e60-a00c8a8b140c · inbound

Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation cites this paper.

Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T14:00:07.898848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:00:07.898848Z digest=sha256:b4257dd98a786863c09fc3470f2c2271d86c9b08233f89b0ade4e292d16d0288

Observation 71c17b2b-a50e-4d4a-9cdd-a44fb3bd3a75 · inbound

The Wording Effect: Quantifying Two-Way Drift in LLM Benchmark Performance cites this paper.

The Wording Effect: Quantifying Two-Way Drift in LLM Benchmark Performance Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-16T00:39:52.369060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T00:39:52.369060Z digest=sha256:fb50fc56ac6e798672b0d81176e611745bd4c938c355e11a97d39106adc4c5dd