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Paper Citation Record · LEDGER

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

As of 20 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2509.23782.

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

pith.paper-citation-record.v1
2509.23782 v4

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:43:16.659316Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:32:31.846587Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T05:44:38.784930Z

Reference resolution

38 of 38 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4861c87-8192-4d0b-b3f9-7c44bdf9b39e · outbound

This paper cites Understanding intermediate layers using linear classifier probes, 2017.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Understanding intermediate layers using linear classifier probes, 2017

Reference 1

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source=arxiv_source observed=2026-08-04T14:43:10.834850Z digest=sha256:80f2c343f2739f2719f44f28983d23381692c7a92032e7cccca7bc11d03a3e4a

Observation 4fdcf6f6-aa45-452a-be66-1db39a2491e9 · outbound

This paper cites Refusal in language models is mediated by a single direction.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Refusal in language models is mediated by a single direction

Reference 2

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source=arxiv_source observed=2026-08-04T14:43:10.991738Z digest=sha256:23d5cc6fc12f6be2204408edebf578958007d2a38860757061b82a28124c95a6

Observation 32bc3b33-4855-4780-ac24-39eefbe48f88 · outbound

This paper cites The internal state of an LLM knows when it ' s lying.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions The internal state of an LLM knows when it ' s lying

Reference 3

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source=arxiv_source observed=2026-08-04T14:43:11.125807Z digest=sha256:152d737657fc488dbb64dab53765b33176e168a7ea3b49fda91765e85017f1cc

Observation a4dbe1c3-1615-497c-840c-7e3b527c8fd0 · outbound

This paper cites Probing the geometry of truth: Consistency and generalization of truth directions in LLM s across logical transformations and question answering tasks.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Probing the geometry of truth: Consistency and generalization of truth directions in LLM s across logical transformations and question answering tasks

Reference 4

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source=arxiv_source observed=2026-08-04T14:43:11.254749Z digest=sha256:197d47a8e9361f9908e76124702e48e1635b05587dfca57c09d1c4c437e60823

Observation e5cbb389-ec1f-41e0-a528-85b13efbd060 · outbound

This paper cites an unresolved cited work.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-04T14:43:11.344855Z digest=sha256:99b439281644a1d48e30926482cba597db8b77243af7ba7ea5a6651307475393

Observation 7c5363e6-c712-4c37-aa28-cbdd340f86bd · outbound

This paper cites Answer Matching Outperforms Multiple Choice for Language Model Evaluation.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Answer Matching Outperforms Multiple Choice for Language Model Evaluation

Reference 6

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source=arxiv_source observed=2026-08-04T14:43:11.483898Z digest=sha256:63243291b1abbf44f6031e6177d10a7de589efe05bb66617307a536928a7fdb8

Observation b7cd00fa-fa5a-4de1-95df-e54156878fea · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Gonzalez, Ion Stoica, and Eric P

Reference 7

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source=arxiv_source observed=2026-08-04T14:43:11.584755Z digest=sha256:26f21cb379458202b06c0273842bf833d1af5c737fd0715e751ad353212b0d77

Observation a6265235-1739-4e7b-b4a7-88de8c1336db · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 8

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source=arxiv_source observed=2026-08-04T14:43:11.694751Z digest=sha256:d8078603eda4b8882b0da3a1697d1097289dc557efcf3a770a40335ba10b32d5

Observation f6895e65-dd42-46a4-929b-509c7c8bc0b1 · outbound

This paper cites Knowledge neurons in pretrained transformers.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Knowledge neurons in pretrained transformers

Reference 9

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source=arxiv_source observed=2026-08-04T14:43:11.774755Z digest=sha256:38b9a5a4199131e4a37ec287eea56cb7a3312c4c76e045d5229e4ed891bdd42b

Observation aa81f760-e8e4-4f17-8f7e-2121feb525a4 · outbound

This paper cites Plug and play language models: A simple approach to controlled text generation.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Plug and play language models: A simple approach to controlled text generation

Reference 10

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source=arxiv_source observed=2026-08-04T14:43:11.954755Z digest=sha256:822adc2d867682905bd1289482fc9805454af447cc0c636ba1ae4b39a4c46dbf

Observation bb0ef333-e192-411f-9aa1-fae9edeb02cb · outbound

This paper cites In-Context Learning Creates Task Vectors.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions In-Context Learning Creates Task Vectors

Reference 11

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source=arxiv_source observed=2026-08-04T14:43:12.104751Z digest=sha256:5d20ba0661f25df7bf6bb0f3afee56a3126fda3c925a269645173c2db7f110ae

Observation fb4b5cdf-99b3-4f2c-a364-4d2c26d97398 · outbound

This paper cites Measuring massive multitask language understanding.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Measuring massive multitask language understanding

Reference 12

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source=arxiv_source observed=2026-08-04T14:43:12.266840Z digest=sha256:30d2a1255b5fd12c47e9017e7dd77509174242ec737d1b0d13e79024c96ad60b

Observation 2d4ef28b-d407-497e-8804-bcffa2d9fc7a · outbound

This paper cites Lo RA : Low-rank adaptation of large language models.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Lo RA : Low-rank adaptation of large language models

Reference 13

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source=arxiv_source observed=2026-08-04T14:43:12.474741Z digest=sha256:d8dfd73d8a7e5f09acbc0f3838a4b4bdeb6e7b4544369f3610290db84f6a66f4

Observation 4c8bab94-f220-462c-976b-10211c9efe78 · outbound

This paper cites Editing Models with Task Arithmetic.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Editing Models with Task Arithmetic

Reference 14

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Observation 8c20cf2e-6896-4ecb-8776-5cd00b577bf0 · outbound

This paper cites Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre Dognin, Manish Nagireddy, and Amit Dhurandhar.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre Dognin, Manish Nagireddy, and Amit Dhurandhar

Reference 15

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source=arxiv_source observed=2026-08-04T14:43:12.639855Z digest=sha256:4d3934225475347eea02b345a2b119746c4f5575f7a25ad8f25217af5e5503b2

Observation 0e449c57-2bbf-41e4-a5c8-552fc7727168 · outbound

This paper cites Inference-time intervention: Eliciting truthful answers from a language model.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Inference-time intervention: Eliciting truthful answers from a language model

Reference 16

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source=arxiv_source observed=2026-08-04T14:43:12.724755Z digest=sha256:ab39b88db1d38b7bfb3de563fafee305f5861454933b14af7399cfe259585c90

Observation dfb45d73-c2fc-4ae4-8573-2f0168c57871 · outbound

This paper cites an unresolved cited work.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Unresolved cited work

Reference 17

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source=arxiv_source observed=2026-08-04T14:43:12.824752Z digest=sha256:7f913f37dc665a75e705351e8870de290f1f043468eb22bc7b3f577ce6245fc5

Observation bd241c84-4760-4714-894c-81c6e1b8090f · outbound

This paper cites Investigating Bias Representations in Llama 2 Chat via Activation Steering.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Investigating Bias Representations in Llama 2 Chat via Activation Steering

Reference 18

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source=arxiv_source observed=2026-08-04T14:43:12.984764Z digest=sha256:837cde4e7d88fc929f1845bac1519d6c6d1178188929918b45854049d992cd24

Observation 8ef1facb-36b6-4280-aa62-f53b12ec9d0a · outbound

This paper cites The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Reference 19

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source=arxiv_source observed=2026-08-04T14:43:13.214435Z digest=sha256:aaf48095f896c3fb9bdf65c8761e842df2eba33652f4556e6bc4ff405d0871b1

Observation 12f9e328-d523-40cf-a968-0143306db352 · outbound

This paper cites Locating and editing factual associations in gpt.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Locating and editing factual associations in gpt

Reference 20

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Observation 6e949ff8-2c33-4a7a-a8d8-bee0c97f1101 · outbound

This paper cites Introducing gpt-5.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Introducing gpt-5

Reference 21

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source=arxiv_source observed=2026-08-04T14:43:13.554750Z digest=sha256:859c4cf77b3139e65401ce785159d9a7506381d6330f9599f84a422ee1b8d351

Observation 769231bb-d2b5-4585-9799-61887f627ff2 · outbound

This paper cites LLM s know more than they show: On the intrinsic representation of LLM hallucinations.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions LLM s know more than they show: On the intrinsic representation of LLM hallucinations

Reference 22

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source=arxiv_source observed=2026-08-04T14:43:13.676180Z digest=sha256:8456729f2d43b60552ab49e2674a1115e7156483ae7c70f258d8989cad864e6f

Observation 89f8d6d1-c388-471b-901a-1952b944fca0 · outbound

This paper cites Large language models sensitivity to the order of options in multiple-choice questions.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Large language models sensitivity to the order of options in multiple-choice questions

Reference 23

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source=arxiv_source observed=2026-08-04T14:43:13.894756Z digest=sha256:79ca0d2c43b4c0a6a661a85d9acf30fbdebfd502c3ac9e9a4a02dc7a3ab3dacc

Observation 05c47a6d-4b5b-49c8-8907-a5480d2c89a6 · outbound

This paper cites Qwen2.5 Technical Report.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Qwen2.5 Technical Report

Reference 24

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source=arxiv_source observed=2026-08-04T14:43:14.064827Z digest=sha256:f91faece74babffbd75ac3f02c6bc34ca89820ca48bc9dc6eedb87260885c9bd

Observation d8311e3c-ea13-4676-b1a3-10d99d7b1a63 · outbound

This paper cites Steering llama 2 via contrastive activation addition.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Steering llama 2 via contrastive activation addition

Reference 25

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source=arxiv_source observed=2026-08-04T14:43:14.245491Z digest=sha256:c2292731ebd3d54993177bb70b14fd941a9badca421b97aec01980a034a78652

Observation 42bb0af7-1ac2-481a-b0e3-9d8bd3a65377 · outbound

This paper cites Layer by layer: Uncovering hidden representations in language models.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Layer by layer: Uncovering hidden representations in language models

Reference 26

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source=arxiv_source observed=2026-08-04T14:43:14.444798Z digest=sha256:57fadfc9dd4def1fcf927f18ac2e7eb57679ad002e72e8e63cf99ba0e3d7ae51

Observation be4e53b3-807c-4d95-ad07-7ae34df46d9f · outbound

This paper cites Unsupervised real-time hallucination detection based on the internal states of large language models.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Unsupervised real-time hallucination detection based on the internal states of large language models

Reference 27

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source=arxiv_source observed=2026-08-04T14:43:14.604749Z digest=sha256:bfafe313c2d602454683fe87430f983031718b684c7c6b258221c3106182bc50

Observation de047cd8-163f-4db8-834e-aef3550c8341 · outbound

This paper cites Challenging BIG -bench tasks and whether chain-of-thought can solve them.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Challenging BIG -bench tasks and whether chain-of-thought can solve them

Reference 28

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source=arxiv_source observed=2026-08-04T14:43:14.784754Z digest=sha256:8096adb1e6f51917cd83985a8ee006a4a0f2635884158015b7630e0e5a5f1516

Observation e2e3c5ee-8a7c-4fc2-a8b9-8080c4ee664a · outbound

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

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

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source=arxiv_source observed=2026-08-04T14:43:14.974820Z digest=sha256:8b43bd2f212752227af744c6739fe9f313f6f4455cb0c0da6174824008030730

Observation e7997358-7fa8-421c-95c0-603d15ad6dfe · outbound

This paper cites Steering language models with activation engineering, 2025.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Steering language models with activation engineering, 2025

Reference 30

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source=arxiv_source observed=2026-08-04T14:43:15.134753Z digest=sha256:860f026d1f65d20b4263d719270e9ee864fa4e22520fd826b32c8c24c8e33fcd

Observation f7e4287d-2a93-4639-bea7-5c0ce74d9d53 · outbound

This paper cites FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models

Reference 31

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local_arxiv, observed 2026-08-04T14:43:27.887348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-04T14:43:15.220082Z digest=sha256:5edf674c1b007af2cc3cc2b1d7e96c814dd04d2bda2e3de84b99c58c85827ccd

Observation b18fa2fb-756c-40fe-b15a-80ee93c497b5 · outbound

This paper cites Chi, Quoc V.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Chi, Quoc V

Reference 32

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source=arxiv_source observed=2026-08-04T14:43:15.404755Z digest=sha256:a115cfdbe8a4a7f7f55c00386b76413c313589f08c224021f3dbe8bb5cd4ad64

Observation 2861dfea-6645-4832-901a-d4f7d7302003 · outbound

This paper cites Large language models are not robust multiple choice selectors.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Large language models are not robust multiple choice selectors

Reference 33

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source=arxiv_source observed=2026-08-04T14:43:15.575227Z digest=sha256:bf38f34b613e0d19ceb2c59306cdf9770e6676975dba4da2491c08d6acdd8087

Observation 1724327b-ea3d-412a-899c-f0b9ff65cad1 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Representation Engineering: A Top-Down Approach to AI Transparency

Reference 34

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source=arxiv_source observed=2026-08-04T14:43:15.684752Z digest=sha256:a810fcd4e092716269a8694bac36b3cb439a3b06d7b904fab3747c8e36e5370d

Observation acb733d1-9244-48e3-ba78-0aed45ec7483 · outbound

This paper cites write newline.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions write newline

Reference 35

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source=arxiv_source observed=2026-08-04T14:43:15.874752Z digest=sha256:2bcf21c3dbfc2e64bc0199ed04006128bb511a6a07322638fd39498dc66a87c3

Observation 44f902df-2182-40a0-b442-5ef5e894cd62 · outbound

This paper cites @esa (Ref.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions @esa (Ref

Reference 36

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source=arxiv_source observed=2026-08-04T14:43:16.134895Z digest=sha256:a74b01a6790cf7569079a0f7267c0eba6e3873101750177b7498da0989ff74e8

Observation 7a728661-918c-4066-9d0e-5049b5fe8a3d · outbound

This paper cites an unresolved cited work.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Unresolved cited work

Reference 37

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source=arxiv_source observed=2026-08-04T14:43:16.324755Z digest=sha256:d54fd6185bdf3d6c4a7ec73fb5018a0ab0230c1bfb1e5d8660116285f938f083

Observation 07a60ae9-8d01-490c-8a93-19b8e2ee472b · outbound

This paper cites an unresolved cited work.

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions Unresolved cited work

Reference 38

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no resolver link, observed 2026-08-04T14:43:16.659316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T14:43:16.659316Z digest=sha256:c796782ab9a5a8ec6ca41635c5ed86d4c5ec986a0b8b8f32e7315b683a5cba00

Pith citing papers

Observation b0fd5d4b-2ae4-4e1d-ab70-815f025a85b4 · inbound

Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs cites this paper.

Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-06-02T03:04:01.145620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-22T05:41:39.396469Z digest=sha256:46df5ab9ec9a3447afa615ebfa5c41cf6c04c34fe9864daa62d65abf5784db08

Observation a16442fd-35b7-4eb5-b923-cc08be8f495a · inbound

Separating Decision-Rule Misalignment from Readout-Coverage Limitations in Speech Language Models cites this paper.

Separating Decision-Rule Misalignment from Readout-Coverage Limitations in Speech Language Models Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-10T04:32:31.846587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:32:31.846587Z digest=sha256:f02b676af67d19c0b5b5981801f1bf7a35509cb10e483580bb6a5f796fb9ada4