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Paper Citation Record · LEDGER

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2507.19525.

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

pith.paper-citation-record.v1
2507.19525 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:51:17.233907Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T10:05:29.093803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T10:07:43.266061Z

Reference resolution

54 of 54 outbound references displayed

  • verified exact4
  • verified fuzzy17
  • unresolved33
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a419aeb3-04f8-4616-8ac4-741362428b3c · outbound

This paper cites ChipNeMo: Domain-Adapted LLMs for Chip Design.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs ChipNeMo: Domain-Adapted LLMs for Chip Design

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:51:16.912733Z digest=sha256:63277ddc34c1fc3f3646226a6c9b3f442afdbd8145036c4c898635b5115bbe5b

Observation 109b7cfc-d319-40ae-8bf0-d11fa5b1fcaa · outbound

This paper cites SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model

Reference 2

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verified exact
local_arxiv, observed 2026-08-06T15:51:17.895784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.921758Z digest=sha256:05e6dc7775c02d8f6d563c5c67233bc6bfef2a0cd46194ac6262900339764d43

Observation 08ade2e4-3b06-4b60-9559-4002daf21789 · outbound

This paper cites Openllm-rtl: Open dataset and benchmark for llm-aided design rtl generation,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Openllm-rtl: Open dataset and benchmark for llm-aided design rtl generation,

Reference 3

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raw_fallback, observed 2026-08-06T15:51:18.221774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.929211Z digest=sha256:f095e5e66ae011f5a86a5850ac01651332bdb21c0a47dc4f720ef18b99b802d3

Observation 3cac6b90-f9e0-49a8-b66e-d70dcd92e8d5 · outbound

This paper cites Rtllm: An open-source benchmark for design rtl generation with large language model,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Rtllm: An open-source benchmark for design rtl generation with large language model,

Reference 4

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raw_fallback, observed 2026-08-06T15:51:18.205192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.935433Z digest=sha256:a6f2c67a5a9fa0f0d3769a2e972329326e90e4d689b6d9b6f9f278fc3e574f04

Observation 128a57b8-370b-439e-816d-287ba2eb020c · outbound

This paper cites Autobench: Automatic testbench generation and evaluation using llms for hdl design,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Autobench: Automatic testbench generation and evaluation using llms for hdl design,

Reference 5

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raw_fallback, observed 2026-08-06T15:51:18.189676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.941944Z digest=sha256:c3e4ff3cb3d73a5b5ca88d5ec0d9d1070afde031362a3ac3e13118cc3f55b014

Observation 5497fd4a-983a-4cd8-958f-1edcd8df0bcf · outbound

This paper cites Verilogeval: Evaluating large language models for verilog code generation,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Verilogeval: Evaluating large language models for verilog code generation,

Reference 6

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raw_fallback, observed 2026-08-06T15:51:18.173671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.946817Z digest=sha256:44021b49d8849586b0c58c9399e0367fca83e396e973fd6b8a2dce94d148846b

Observation 578eb82d-8759-41f0-9146-e68449726ef0 · outbound

This paper cites DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:51:16.952647Z digest=sha256:f010dcfdb02a659949ebd789440ab268e0872d43f48d1e87dc2a313214475ff7

Observation d446026a-cc98-40e6-a88a-01818d6d4fd5 · outbound

This paper cites EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD

Reference 8

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local_arxiv, observed 2026-08-06T15:51:17.857997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.958395Z digest=sha256:8dc03fa3c619e1c1adacd11a71f47c303003bdae7af4bb158066281a9f75fb75

Observation addf7d73-a50b-4111-be2a-a180c18ebd65 · outbound

This paper cites Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA

Reference 9

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source=pdf_text observed=2026-08-06T15:51:16.964368Z digest=sha256:e5931beb19ed97948430135706306e241990a1ac6300052290b0e3fe29455009

Observation 844bcb60-1ed6-4c4c-83d8-e874fe94597c · outbound

This paper cites The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 10

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source=pdf_text observed=2026-08-06T15:51:16.969771Z digest=sha256:65595e048c8c2f9d07ac01275b9712c832fd6e6f4a653a3c4acd02df67b4f724

Observation 25ed9432-ebe7-4fb9-8cd6-b3bc9c2ea2ae · outbound

This paper cites Deepgate: Learning neural representations of logic gates,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Deepgate: Learning neural representations of logic gates,

Reference 11

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raw_fallback, observed 2026-08-06T15:51:18.158615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.975053Z digest=sha256:fe286027c5992987c4aaa530f2aeff5d6b52fe679b7576d96393d61cbd1e6f5e

Observation 15de77ff-e2be-4554-9368-f4a9344a48fd · outbound

This paper cites Deepgate2: Functionality-aware circuit representation learning,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Deepgate2: Functionality-aware circuit representation learning,

Reference 12

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raw_fallback, observed 2026-08-06T15:51:18.143095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.981378Z digest=sha256:c9205fba10b21f8192685a383afc2b6affe8d4cba2fb6e32ba77542aa58a4109

Observation ee7fdac5-75a8-4a96-8dbf-e3dde19240ac · outbound

This paper cites DeepGate3: Towards Scalable Circuit Representation Learning.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs DeepGate3: Towards Scalable Circuit Representation Learning

Reference 13

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verified exact
local_arxiv, observed 2026-08-06T15:51:17.805475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.988229Z digest=sha256:49640b88c7a672ba6fa5f70a0da303159f8e61e8464f776b9d658f48782928cf

Observation bf6167ff-96be-4d75-88cb-07bd972987c8 · outbound

This paper cites DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale

Reference 14

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verified exact
local_arxiv, observed 2026-08-06T15:51:17.782618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:16.994747Z digest=sha256:455c124a4c31ff83a5ecd965ca3210f189e1c0db22395e04fec7aa8ec9ba6776

Observation 2ee24dd8-da72-4218-9edd-eb7b6fbc3eb1 · outbound

This paper cites AutoChip: Automating HDL Generation Using LLM Feedback.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs AutoChip: Automating HDL Generation Using LLM Feedback

Reference 15

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source=pdf_text observed=2026-08-06T15:51:17.000553Z digest=sha256:838bf23f51b03a6ef2f00aae642feccb4ec3a64c68b8e375a0262101a00bb79b

Observation e524bd91-5f36-4e73-87b7-b743cb198362 · outbound

This paper cites Rtl- coder: Fully open-source and efficient llm-assisted rtl code generation technique,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Rtl- coder: Fully open-source and efficient llm-assisted rtl code generation technique,

Reference 16

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source=pdf_text observed=2026-08-06T15:51:17.006595Z digest=sha256:a674065c916fe0595ff60efc4e33022b9b01190fa4a681759a2853b4cb80f143

Observation d5e31a20-df2e-4bc7-9ef5-9037bb65b86f · outbound

This paper cites Verigen: A large language model for verilog code generation,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Verigen: A large language model for verilog code generation,

Reference 17

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raw_fallback, observed 2026-08-06T15:51:18.116952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.011689Z digest=sha256:4289c3c678b8d4d03adb16285b861f26b80771a2255c90f75d291cb9ec901703

Observation 75256a78-6bea-4ff5-80ec-014340228ea8 · outbound

This paper cites AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting

Reference 18

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T15:51:17.017168Z digest=sha256:b9a560f760304ad8069b8fd64823ac0d696d5bb213f99d028b07beff86465477

Observation 72fe7c05-57aa-4060-bee7-0823bcd74789 · outbound

This paper cites Chateda: A large language model powered autonomous agent for eda,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Chateda: A large language model powered autonomous agent for eda,

Reference 19

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source=pdf_text observed=2026-08-06T15:51:17.024063Z digest=sha256:78090e74e88748a0cb212422b61e56f18b02d06cd80673d48770473f76c6c667

Observation 81083eca-4fa3-4f1f-a900-fa7b8b6b9c81 · outbound

This paper cites Openroad: Toward a self-driving, open-source digital layout implementation tool chain,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Openroad: Toward a self-driving, open-source digital layout implementation tool chain,

Reference 20

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raw_fallback, observed 2026-08-06T15:51:18.085480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.030673Z digest=sha256:17bce0b04e7ada0d97f159496d2b4e8227d05993a6d1589584168689a2f84009

Observation 094d4aef-15c0-42ba-8820-73b181ffd74c · outbound

This paper cites Chipgpt: How far are we from natural language hardware design,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Chipgpt: How far are we from natural language hardware design,

Reference 21

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source=pdf_text observed=2026-08-06T15:51:17.039330Z digest=sha256:843e4b1ae7d7432f9c7e14b154e90e1e1d1d65f1ba14cf60e9da46faec285c37

Observation ead691cd-84ac-4ba2-846e-7795243e2fb8 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:51:17.045621Z digest=sha256:44fcedc9293dfe18b16804cfc6428e40a02b54d7bd935df28a8043aff4bc23f5

Observation 71178a86-0847-4ee5-84d2-c5027c0159b3 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,

Reference 23

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raw_fallback, observed 2026-08-06T15:51:18.070119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.052390Z digest=sha256:acc637b1be5762bc43f8201b504dcb5b68435f584ad1fc86f6c0ff9ed9ba1db2

Observation 1dfc5907-444a-4fd8-9e26-a0bc3e54681d · outbound

This paper cites Mm- bench: Is your multi-modal model an all-around player?.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Mm- bench: Is your multi-modal model an all-around player?

Reference 24

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.058059Z digest=sha256:f5a2a3b069f6df7bf0ed47d2f8d288ca062afd1cf8cdbfd3995d3886b4901018

Observation bb92531a-a3e9-4e0f-a306-8f795430904c · outbound

This paper cites ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model

Reference 25

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T15:51:17.063404Z digest=sha256:5097d32d4c58836d2bc09b033aad093bbe2055dfd4c30eab023dc09486b17e3c

Observation cbc7941f-5ab6-42c1-8c04-c5911f47f3e2 · outbound

This paper cites GPT-4 Technical Report.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs GPT-4 Technical Report

Reference 26

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no resolver link, observed 2026-08-06T15:51:17.069313Z

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source=pdf_text observed=2026-08-06T15:51:17.069313Z digest=sha256:6ab8f4dc6dd16cc55b5b2f252331f3dc2e97211b90229d579ab5ce139b5f86b6

Observation 3f2a9dc3-c3c6-42fa-abe0-7d28c7d65373 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 27

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raw_fallback, observed 2026-08-06T15:51:18.040918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.078106Z digest=sha256:a67773d7aabc283f1905d3829025eb52916cdeb1490dd1671bdca303c1f9eb6e

Observation ab6d3375-d6ab-48a0-8b36-dc6dce52fc13 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 28

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:51:17.093914Z digest=sha256:4563f44479fd95846c9eb7b01ec731077072f018cf5ce0b72e20eaa1f1d77a30

Observation 4b609da0-9d61-4d75-be1e-dabb843144db · outbound

This paper cites InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition

Reference 29

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source=pdf_text observed=2026-08-06T15:51:17.099887Z digest=sha256:7ae0478348e57ee4ba98c53c4cf4c9d6bc72da17ca3296e796344300b676e182

Observation e1ebf3e1-1969-457d-8fb5-c122916c0aab · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,

Reference 30

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raw_fallback, observed 2026-08-06T15:51:18.026078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.106267Z digest=sha256:63c0d191d8bc97a1692f2af89a669e8c8caf6f45b98f0ea629583b3cc33295b5

Observation 729b1d09-85f8-46d2-812c-64754912dbbd · outbound

This paper cites Instructblip: Towards general-purpose vision- language models with instruction tuning,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Instructblip: Towards general-purpose vision- language models with instruction tuning,

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:51:17.111470Z digest=sha256:d344cbf8e4fbd942a9756d6d86eb11c192efd46634546b7b7d7830bed9c6a307

Observation 3db087b0-fa50-4033-9d16-ecdf5aaf34a0 · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 32

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raw_fallback, observed 2026-08-06T15:51:18.000768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.116280Z digest=sha256:065419e9513a054e49b8b3ac4187b45c0c8e0aeccbcb384ba75ccf84c68ca466

Observation 24fa938f-bb6b-47b4-bb43-dd678a955ea0 · outbound

This paper cites The Llama 3 Herd of Models.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs The Llama 3 Herd of Models

Reference 33

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source=pdf_text observed=2026-08-06T15:51:17.121764Z digest=sha256:a1ff1a3bd27dccb0da9fa8e5cde82d6ed7cf76257b06c5c14a3ecfeccb77333e

Observation f6c0f823-15fa-49da-a62a-7b3c0a3a9398 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 34

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source=pdf_text observed=2026-08-06T15:51:17.128128Z digest=sha256:55b05dc98baaf4ecf88c8c48df847f24834eca328b2810d983e77aed00526fec

Observation 8a2bb5f9-0d34-4f23-bcf0-7ebf6215c6b6 · outbound

This paper cites Yi: Open foundation models by 01.ai,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Yi: Open foundation models by 01.ai,

Reference 35

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.133324Z digest=sha256:72310723bcab160bc150f2e4bc01418f38f98f818106c0947717b39ecfe27fe1

Observation cb79e9a6-4927-43e7-a8af-8f321bd31445 · outbound

This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 36

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source=pdf_text observed=2026-08-06T15:51:17.139099Z digest=sha256:4bb0e8ac2111e7abd92623aa2e23d44f40614c05463970ea5d66a83f9921135f

Observation 731c1bb5-9582-4676-b119-93fabf117ea5 · outbound

This paper cites GPT-4o System Card.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs GPT-4o System Card

Reference 37

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source=pdf_text observed=2026-08-06T15:51:17.143992Z digest=sha256:929f4febada5d16b06a3e09f77f79df5010b0885702b47f6f7e0e98d8875dd98

Observation 3bf026fa-d614-4338-bdd4-9ba4f8da7982 · outbound

This paper cites Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models

Reference 38

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source=pdf_text observed=2026-08-06T15:51:17.148602Z digest=sha256:7570a8a2a33f040441b0e268756e3c24ff5818de3e2c4983bbda15e957c71ba5

Observation a27ef1f9-599c-4706-abc2-9c6205d59228 · outbound

This paper cites Training language models to follow instructions with human feedback,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Training language models to follow instructions with human feedback,

Reference 39

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.154151Z digest=sha256:60e5591ffdbe0b991eeca5eec56420519571061e7f42eab32eee8b9cfc8387ce

Observation 3ad89559-04f8-4e8a-ade6-47f2d293a4e5 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 40

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source=pdf_text observed=2026-08-06T15:51:17.159030Z digest=sha256:63b5177ce6f734c4c211ec803d879538fabedb82623f5671f59c5a6e4dcf69b8

Observation 87cb3f12-4f8c-4c7e-9cf9-79080eba7608 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 41

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source=pdf_text observed=2026-08-06T15:51:17.164744Z digest=sha256:ca13f627f0fd15733e317ace48408c720b2f04c48590e71b56f3ac6d7984803d

Observation 18563847-c589-408f-8929-13531c10b2fb · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 42

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source=pdf_text observed=2026-08-06T15:51:17.169647Z digest=sha256:004e9edcf94fdcc1e05256ec798e455305e02ab71b96553ab3006f5094976a79

Observation be387cce-c2e0-495a-b380-dec5915f6754 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 43

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source=pdf_text observed=2026-08-06T15:51:17.174974Z digest=sha256:d69e4f02be9951677056c633b811170ebc25e9ead6117bdf1262d09eee026e90

Observation eb7b6998-2552-42ad-8b06-cd08ecb5e814 · outbound

This paper cites Qwen2 Technical Report.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Qwen2 Technical Report

Reference 44

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source=pdf_text observed=2026-08-06T15:51:17.180690Z digest=sha256:b0043ccda5b62f2c164638144fb84e2969cf56f008f1d946718b0146237d775d

Observation 011286e8-6a12-4703-b35b-f0bc1f4b1f53 · outbound

This paper cites Internlm: A multilingual language model with progressively enhanced capabilities,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Internlm: A multilingual language model with progressively enhanced capabilities,

Reference 45

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source=pdf_text observed=2026-08-06T15:51:17.186161Z digest=sha256:72511c5d75a5c7c10dd942ed0a4c5e7ada87497db394802c3cab2edd70603b51

Observation a4c04907-5961-4cb7-a091-8752e214aba7 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 46

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source=pdf_text observed=2026-08-06T15:51:17.190937Z digest=sha256:325179fbb25e74f119c099077c0981f03c462c859c268146fffb506ee58a72a3

Observation 71d40203-d16b-431c-b640-effd35a3e47d · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 47

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source=pdf_text observed=2026-08-06T15:51:17.196765Z digest=sha256:3659fbcbc7e0cc75e0a8ad2f680307340780a9609d020868c57b3bf37305e200

Observation ebc3817a-2c46-4dff-a0d0-8969f3d56eb1 · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 48

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source=pdf_text observed=2026-08-06T15:51:17.202392Z digest=sha256:d1721faf85ade17d82d8171cc90b6172a51158d6bdbb094e3c9121a9431a1af7

Observation 702586f6-6497-4a16-9149-023e470f0276 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Gemini: A Family of Highly Capable Multimodal Models

Reference 49

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source=pdf_text observed=2026-08-06T15:51:17.207829Z digest=sha256:811f3ffc8673916e15bdb481b3fce8b4fe7d5ac8e2f38b3a661e3f9e02c0feec

Observation f7d00419-4ff1-4299-92f8-df8e1db749c0 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 50

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source=pdf_text observed=2026-08-06T15:51:17.212642Z digest=sha256:676d18162309a6974759018d6966fc63fc25212fa15584dcd6a3b58a44eff93f

Observation c33732ac-dd29-46ce-9cc9-3da01af18351 · outbound

This paper cites Introducing the next generation of claude,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Introducing the next generation of claude,

Reference 51

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.218440Z digest=sha256:37bbd3e3cc176288304d6eefa60754ab0dc7eec7410d8e856ac41790af56a9b8

Observation 822042b0-c8c7-4988-a374-0d966cd12b94 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 52

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source=pdf_text observed=2026-08-06T15:51:17.223412Z digest=sha256:a73af0bacaebe75b05814253063b065fa8aa2f1753a773bde12974ef32203bdb

Observation 830ec5db-bf2b-411e-9fdf-e356b33cb029 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 53

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source=pdf_text observed=2026-08-06T15:51:17.229216Z digest=sha256:3fbb615d59ac9c1ae7751f135d3bffe3f5000da8fb8dbb985ebc76d15287c70a

Observation 037edc80-35b4-44b7-93e8-294d2f01ee1e · outbound

This paper cites Language models are few-shot learners,.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Language models are few-shot learners,

Reference 54

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raw_fallback, observed 2026-08-06T15:51:17.929488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T15:51:17.233907Z digest=sha256:55a184a8352f3097787bdc0c5fca8bcc18bfb1c43e0723a067e60885624f8289

Pith citing papers

Observation bc087c9e-bd8a-4f97-b410-0ea88f022682 · inbound

CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis cites this paper.

CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs

Reference 12

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arxiv_id, observed 2026-05-16T10:07:43.268039Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T10:05:29.093803Z digest=sha256:a6b8e0f6c7ac603d6015c0cf3c46e36b18e847fbe04751c31b3db4478bdb5456