Pith. sign in

Paper Citation Record · LEDGER

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration

As of 17 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2505.03985.

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

pith.paper-citation-record.v1
2505.03985 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:45:01.334167Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

  • verified exact0
  • verified fuzzy36
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c94dfeb0-4b10-4f44-bf8c-3e4e618c368a · outbound

This paper cites The power of assessment feedback in teaching and learning: a narrative review and synthesis of the literature.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration The power of assessment feedback in teaching and learning: a narrative review and synthesis of the literature

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.991414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.109925Z digest=sha256:eca22335d824c809b29bdba533e84e41138e4057d1321ae29a53d5678f9c9c4b

Observation b08d3a66-4a8a-4f0d-96ea-7c271c1ee447 · outbound

This paper cites Planning for the unknown: Local government strategies from the fiscal year 2021 budget season in response to the covid-19 pandemic.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Planning for the unknown: Local government strategies from the fiscal year 2021 budget season in response to the covid-19 pandemic

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.977069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.115832Z digest=sha256:967e4eac9aab50147888e32dd32da340794b30b7254e9b00c7240f314cc2431c

Observation 31ab4bbd-564b-4511-bb06-eb897c22adf2 · outbound

This paper cites The value and effectiveness of feedback in improving students' learning and professionalizing teaching in higher education.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration The value and effectiveness of feedback in improving students' learning and professionalizing teaching in higher education

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.965061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.120410Z digest=sha256:2ae446bdb3437abb519e02902d2a606e098a060179c5aef0a993f0dae840cca6

Observation 7e75f048-2abd-443f-bc70-150a509ea2c6 · outbound

This paper cites Why Does the Effective Context Length of LLMs Fall Short?.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Why Does the Effective Context Length of LLMs Fall Short?

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.124930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.124930Z digest=sha256:029a137130f997772f1d308e520c70330e76a0afc8ae4b2a2ffbe41bfe52d276

Observation 2d002b3c-f1af-4b1a-a458-e44e6ff7593e · outbound

This paper cites Combining LLMs with Logic-Based Framework to Explain MCTS.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Combining LLMs with Logic-Based Framework to Explain MCTS

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.129692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.129692Z digest=sha256:0673a02654d62054291352fd77bde77bb70406f9d4922beefa6e7cb411265e4f

Observation 3192c3b6-9ed4-4e1e-b634-f58b0411dbb8 · outbound

This paper cites Bounded model checking of signal temporal logic properties using syntactic separation.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Bounded model checking of signal temporal logic properties using syntactic separation

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.953905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.134259Z digest=sha256:9ee6033fb570c45b43e99c118b45acef98c8494876e3a2e41ad018bcf61e6540

Observation a4bebf1d-2033-4d5e-8d7b-7afd3ade63f0 · outbound

This paper cites Cityspec: An intelligent assistant system for requirement specification in smart cities.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Cityspec: An intelligent assistant system for requirement specification in smart cities

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.942033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.139019Z digest=sha256:cc34ff54fb26cb98afebd7982605c68bf4160c1721234142bb2fcdf87ea87d3a

Observation 82bb25e5-a491-4f94-92fe-43e168732b88 · outbound

This paper cites An intelligent assistant for converting city requirements to formal specification.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration An intelligent assistant for converting city requirements to formal specification

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.929973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.144598Z digest=sha256:e16c6fd5453abcb7ba3a9412fbfa1448eccd455fff3a13c8d557f9860efd7230

Observation 6fe00d47-0a8b-4da8-9691-bfcc0392f27f · outbound

This paper cites Cityspec with shield: A secure intelligent assistant for requirement formalization.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Cityspec with shield: A secure intelligent assistant for requirement formalization

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.917651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.148594Z digest=sha256:f2d511c24090b8cf4324897f86cafa5ebb258ac26480fc3d4b75cdb646d525c0

Observation 25eb9076-7bbd-4ede-8758-afa2801db9b3 · outbound

This paper cites Benchmarking large language models in retrieval-augmented generation.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Benchmarking large language models in retrieval-augmented generation

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.904565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.152543Z digest=sha256:d9ffc07700d55bde8594726e6eb2cdd6f4224e3822ec41c631291bd6db5149df

Observation bb8a96fe-3553-4ff1-9259-1203a1bf2b47 · outbound

This paper cites Auto311: A confidence-guided automated system for non-emergency calls.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Auto311: A confidence-guided automated system for non-emergency calls

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.891300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.156332Z digest=sha256:0e1dac7d4dbfd295555733d5f594bfc275caca2d20767e3e34be65ac971d3d17

Observation bd8185c1-ce80-4881-b5db-e5eb261cde06 · outbound

This paper cites Sim911: Towards effective and equitable 9-1-1 dispatcher training with an llm-enabled simulation.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Sim911: Towards effective and equitable 9-1-1 dispatcher training with an llm-enabled simulation

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.878294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.160339Z digest=sha256:656854f3890da803679fee8b632b2e844ec088cb6d358d324005f29ac9d008ac

Observation 95270411-994a-402e-9a85-f9da88d792d3 · outbound

This paper cites Scaling instruction-finetuned language models.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Scaling instruction-finetuned language models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.164347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.164347Z digest=sha256:2a69aed1714af28cf7b8986013ec8f53a1c84cb84d853e0bcada8ac6edb5b0e7

Observation 236b5ee6-0946-4d87-a534-4571a1bf2334 · outbound

This paper cites nl2spec: interactively translating unstructured natural language to temporal logics with large language models.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration nl2spec: interactively translating unstructured natural language to temporal logics with large language models

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.855942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.168767Z digest=sha256:233395f5676f73c4055da58aa25972b521683ddbb1b71f9fb8f9c2b6408e33af

Observation 8ba31872-2609-4d57-8f27-ad9d3ad4553c · outbound

This paper cites Gemini-flash-thinking: Multi-modal reasoning with external memory.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Gemini-flash-thinking: Multi-modal reasoning with external memory

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.843869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.172909Z digest=sha256:676bc19c6360159c47033acc2480ce0f010ec785d2301ee2ba1738ab01bc00cb

Observation 68f2a7fc-41ef-4461-a30c-15ea68b00c2b · outbound

This paper cites Deepseek-v3: Scaling open large language models with moe, 2024.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Deepseek-v3: Scaling open large language models with moe, 2024

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.832766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.177091Z digest=sha256:7779527610d2dd4384c90474de8f689a44c9a7eda0b496dfa8c8aeba44a8b85a

Observation ac7ef087-d7a6-4225-837d-365f55f5543d · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.820877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.181267Z digest=sha256:94b0e6e29deaf6d58e77f0f111c0d94e39da9e1f94163b3b65ae9bea6f67e943

Observation 326b05f6-8471-4a69-8569-2d2117aba836 · outbound

This paper cites Exploring Context Window of Large Language Models via Decomposed Positional Vectors.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Exploring Context Window of Large Language Models via Decomposed Positional Vectors

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.185583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.185583Z digest=sha256:7fa18b3884125d5829872a61fdffb5f4afe83082c812fc04e6b680ead8de98d3

Observation cc234777-879c-4da0-a525-7e22d421d5ee · outbound

This paper cites An automated system repair framework with signal temporal logic.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration An automated system repair framework with signal temporal logic

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.808593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.190220Z digest=sha256:119d1881dabda03a9393070376cd83047113cc512f6e04a8236b95d920ccaf3d

Observation 7de13c20-4641-455f-8b7e-cdfae9da87f9 · outbound

This paper cites Gemma 2: Improving open language models at a practical size, 2024.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Gemma 2: Improving open language models at a practical size, 2024

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.795658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.198142Z digest=sha256:3fcf3eef3d0656555d76b9dfe7a5ed108f7627059eb2e2181527c1083d1f682e

Observation 15add7da-9352-4fab-b372-1ce7e1fcc71a · outbound

This paper cites Intelligent tutoring systems.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Intelligent tutoring systems

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.783192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.203664Z digest=sha256:baf356cb7504a99c26bc48cc316492e5a75d051d82876b21678db110af27b42e

Observation 9969d227-867b-4886-8863-418b6e97068b · outbound

This paper cites Large Language Models Cannot Self-Correct Reasoning Yet.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Large Language Models Cannot Self-Correct Reasoning Yet

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.207671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.207671Z digest=sha256:3c27a0b7daf30bba2fe0434dae5dcaae41ccd1243045e09691f674e8870caaeb

Observation 754a1cee-a759-4a06-b732-b9668f8f8c28 · outbound

This paper cites America's 911 workforce is in crisis, 2023.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration America's 911 workforce is in crisis, 2023

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.771048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.213483Z digest=sha256:a10af6cd9cd9a286df7de3d6274aa5b3c19ba719314be3632f6e23febc1d0327

Observation 30b1890c-4058-4775-a4a2-b591c3381aef · outbound

This paper cites Call volume and quality assurance challenges in u.s.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Call volume and quality assurance challenges in u.s

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.758863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.217374Z digest=sha256:24d98dcced670a7028241698118405c9e4f9d65ec93b968e02e8c19193f1df39

Observation 97660553-87ef-4860-9e9e-805231dc898f · outbound

This paper cites Can large language models reason and plan? Annals of the New York Academy of Sciences , 1534(1):15--18, 2024.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Can large language models reason and plan? Annals of the New York Academy of Sciences , 1534(1):15--18, 2024

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.746799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.221793Z digest=sha256:7fcbe8e8dad8e13f24737d4d703f2954b9dd8e8454525261972169d59c9f8cc4

Observation 77f1e9c6-dc3f-4462-b8e7-56d316892dd6 · outbound

This paper cites BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.225948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.225948Z digest=sha256:4d5771023100b7874e184ae7581f673489950774b0459c1443d5a116baa5bf3a

Observation 2dbc5671-c818-4114-b4c1-09ca6f4da008 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.230822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.230822Z digest=sha256:df92e869c9e7ea655c0dd64f5c4c82a29435aa9aa0c8432a96e94ad0495f7cfb

Observation 22f69344-c4e7-4ee8-a694-53fea09ff221 · outbound

This paper cites Deductive verification of chain-of-thought reasoning.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Deductive verification of chain-of-thought reasoning

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.724387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.234913Z digest=sha256:760c4fce21598db6c11e866ed5d21661a19de2625ecef94a1a5d0ec50fbc74ab

Observation 8ffed7b3-8381-4e8c-96ae-6d81611069c0 · outbound

This paper cites Lost in the middle: How language models use long contexts.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Lost in the middle: How language models use long contexts

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.709941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.239158Z digest=sha256:81b0336b69ac5e17f2748ae7cf217aa234d85f3aea21a6efafe5a8d0aac92ee2

Observation 4938c3c4-09e6-48d4-943a-1cb85a76f57e · outbound

This paper cites Data sets, modeling, and decision making in smart cities: A survey.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Data sets, modeling, and decision making in smart cities: A survey

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.695183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.242941Z digest=sha256:ec2e734f90642467dbe2419da05c8c561b957d4b691f1eb81b68aeaf576a058b

Observation f267fa0a-b3e3-461d-9f61-671343551534 · outbound

This paper cites Monitoring temporal properties of continuous signals.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Monitoring temporal properties of continuous signals

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.682216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.246853Z digest=sha256:49f0cd160b314f40621c9f400e0c2c33087170ce088f6454e52ec03c94cd2c71

Observation 643d080d-067c-4947-86cd-5b8e88287bcc · outbound

This paper cites When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.250434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.250434Z digest=sha256:00f9c21ea5a9abf58c8a7d38fdf8d275d36991ebe7abe304ee1a436b1bc5c14b

Observation a0d17f2b-7779-4f52-98cb-dd28e5ba70b5 · outbound

This paper cites A critical review of simulation-based medical education research: 2003--2009.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration A critical review of simulation-based medical education research: 2003--2009

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.669452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.254451Z digest=sha256:9ac55230590a11377bf0bc23f31bde198b4bab5e9d01a9af3b6d116aba25cc08

Observation 4050c57f-4c4f-47e5-9dc2-c132eb7ff2f9 · outbound

This paper cites Llama 3.2: Revolutionizing edge ai and vision with open, customizable models, 2024.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Llama 3.2: Revolutionizing edge ai and vision with open, customizable models, 2024

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.258164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.258164Z digest=sha256:4abb0ef6b04425febb44945e23f718b587860003405851b42c6cbece0bfc690e

Observation ca801f2a-64b9-4f74-8d5a-2e9d1fd02869 · outbound

This paper cites SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.261794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.261794Z digest=sha256:8565ff5bde87bd3bcaf7490aa5d855801f6d6dd331e91dbd74f02a4fe26a192f

Observation 3e07374c-c0ac-4b41-82ee-dd1a0218699a · outbound

This paper cites Explainable reinforcement learning: A survey and comparative review.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Explainable reinforcement learning: A survey and comparative review

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.649666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.267067Z digest=sha256:5eb7ac53796d001ce394ea202a505f41e1b1b831e7dc13bee45800a5a81261fb

Observation 5935fa6d-1d03-4444-99e2-e85c65517ed1 · outbound

This paper cites FDNY Issue Brief , 2025.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration FDNY Issue Brief , 2025

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.638274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.271402Z digest=sha256:d01a0deb5f5b0f51fc7d1d13d28d9333693bdbe3311fdbd9c1d8768e0a2f300a

Observation 149cd4ec-4c55-41ae-a72e-f6bbb5144f10 · outbound

This paper cites Gpt-4o system card, 2024.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Gpt-4o system card, 2024

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.276191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.276191Z digest=sha256:88b199c132a9faa56ddc0de6d31c57c169b85817a52db0f3d58467832e0c0153

Observation b0977d8f-4bd3-4755-8dbe-903fb7547cdd · outbound

This paper cites Openai o1 system card, 2024.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Openai o1 system card, 2024

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.280374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.280374Z digest=sha256:8eba62c6f9985a7ed214ebbcdd02b3cfee7325ff1f46bc3bef75f6fff07de864

Observation 1d3b603b-c731-4067-8c09-3b908f75605e · outbound

This paper cites Enhancing text classification through llm-driven active learning and human annotation.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Enhancing text classification through llm-driven active learning and human annotation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.609198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.284223Z digest=sha256:8bdb125aaa87fcb2594475662d9f3a02ac4dd935c10f87d076c932a7c44397d3

Observation 89bf9539-79d8-46c9-b59a-7846e4705b39 · outbound

This paper cites REPLUG: Retrieval-Augmented Black-Box Language Models.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration REPLUG: Retrieval-Augmented Black-Box Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.288081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.288081Z digest=sha256:1a69c3c503fe9c1206927e48790174169d544a02ed4727b2185347e5cd959dca

Observation a32f4086-2faa-4323-8706-91ec1a58e3d4 · outbound

This paper cites Language models that seek for knowledge: Modular search and generation for open-domain question answering.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Language models that seek for knowledge: Modular search and generation for open-domain question answering

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.596161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.291983Z digest=sha256:f13e097b44f90f140626c7c20708ec2117ab2680d2aa81f79ed62d8131b1e3c4

Observation af19df05-11e0-4af4-86e0-3e4115acf609 · outbound

This paper cites Clinical debriefing: a concept analysis.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Clinical debriefing: a concept analysis

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.583518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.295889Z digest=sha256:c400c36b6ec99e3a753139441b8a55bf608b2ad5537b76956e62c3ca43c2ddbf

Observation b24ae4ed-bf0d-4f3d-8e17-7feac76b16ef · outbound

This paper cites Language models don't always say what they think: unfaithful explanations in chain-of-thought prompting.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Language models don't always say what they think: unfaithful explanations in chain-of-thought prompting

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.571790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.300719Z digest=sha256:8edf5bb7569ab8cb7aeff2de0c007a37a045208635978670692ce58b2cdb8e04

Observation 7a657fd3-63d9-4107-b596-e34f9067b8b9 · outbound

This paper cites The behavior of tutoring systems.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration The behavior of tutoring systems

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.558752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.304296Z digest=sha256:fd3f590f8b462cd51f96b5caed58510c715a5b2cfa14cd27034ad007253384ea

Observation ebc86075-6f01-494f-a494-b2c59c4bac1d · outbound

This paper cites Searching for best practices in retrieval-augmented generation.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Searching for best practices in retrieval-augmented generation

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.546770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.308517Z digest=sha256:102a9b22e4a495e2e428a55e88fbccf38eb1c42e2f9c5c9c03110562bffe938c

Observation e3d89924-1c9a-4247-851d-8e877429ad30 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Chain-of-thought prompting elicits reasoning in large language models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.312411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.312411Z digest=sha256:ad819938070d310da5a6181cf9a1c6ab6a31ccbd4db346ae14168a5b95b73293

Observation fb1116b1-ffe1-416d-99e0-c9b3ed2ba50f · outbound

This paper cites Empirical study of llm fine-tuning for text classification in legal document review.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Empirical study of llm fine-tuning for text classification in legal document review

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.526697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.316408Z digest=sha256:25564397facbdaebdfbf9400887a0de57090f7eccbbe4af13aeefdc9d1e505cc

Observation 526f0aba-948c-4fb7-9728-26a8ae6f8e28 · outbound

This paper cites Mastering symbolic operations: Augmenting language models with compiled neural networks.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Mastering symbolic operations: Augmenting language models with compiled neural networks

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.513517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.321083Z digest=sha256:515bab9e4f8c6ddb437916621bff57ddd7e514392c38a3bf3bfabb7fe7d05f4e

Observation d62657d9-23c1-434c-8ff6-825a7c96022d · outbound

This paper cites A Comparative Study on Reasoning Patterns of OpenAI's o1 Model.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration A Comparative Study on Reasoning Patterns of OpenAI's o1 Model

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.325139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.325139Z digest=sha256:1bdb794347734f55ff92e0a9fc1f5e290a4cb4f3c6fac65ba0135d70bd88160c

Observation 0f89b967-0e43-4294-9075-7022a5a3413f · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration Star: Bootstrapping reasoning with reasoning

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:45:01.499245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:45:01.329997Z digest=sha256:1787fd9286b4907a370ba2c4aeaace1eff375fb9fd57e585835608295383fc1c

Observation cbe6e559-d264-4072-8c2a-c7c24cec2b85 · outbound

This paper cites write newline.

LogiDebrief: A Signal-Temporal Logic based Automated Debriefing Approach with Large Language Models Integration write newline

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T23:45:01.334167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:45:01.334167Z digest=sha256:69fb59c64f658b2b966b09fc368f09eec8c91fbe9b7832a4d3a0af47f8aa0240

Pith citing papers

No inbound Pith citation observations are available.