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Paper Citation Record · LEDGER

RoToR: Towards More Reliable Responses for Order-Invariant Inputs

As of 9 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2502.08662.

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

pith.paper-citation-record.v1
2502.08662 v3

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:11:43.306809Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

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

measured 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

49 of 49 outbound references displayed

  • verified exact4
  • verified fuzzy2
  • unresolved41
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0528c8be-f89a-4eb5-9ad1-d402f70a3d27 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 1

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

source=arxiv_source observed=2026-08-08T16:11:43.085052Z digest=sha256:86a342cfb680ca909f03e4b244903e6420e5b9a8d2b5695d578f12a807e016ce

Observation 8075e7fb-d2d2-4739-9b6d-eb560a6331db · outbound

This paper cites When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 2

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source=arxiv_source observed=2026-08-08T16:11:43.090058Z digest=sha256:da5537187785dda3c501917c6e2eb60fe2ceba574b5dbab128144d3853175e3e

Observation 33655849-7d56-47b8-bcb1-57779644c1ff · outbound

This paper cites L-Eval: Instituting Standardized Evaluation for Long Context Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs L-Eval: Instituting Standardized Evaluation for Long Context Language Models

Reference 3

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source=arxiv_source observed=2026-08-08T16:11:43.095401Z digest=sha256:b29124942c6fa4f58a5f9f832029278c6784642533f62c43f0e012818fec94f5

Observation b5a807bb-758f-4e1b-b72e-269030a5cd90 · outbound

This paper cites Knowledge-Augmented Language Model Verification.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Knowledge-Augmented Language Model Verification

Reference 4

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source=arxiv_source observed=2026-08-08T16:11:43.100370Z digest=sha256:0c569f7dcf96aec3c03b5535f292efa7427ff87758430ab14e2dfc6169bf86eb

Observation 69335acc-5830-48fa-89d6-cb225a6c43b0 · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 5

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source=arxiv_source observed=2026-08-08T16:11:43.105251Z digest=sha256:b959399c56d404b31b476408f246a9d20b9d152a931aab43c71ae65cf09d771d

Observation 4b8cf42e-68c4-4a89-9871-7deafe2b646c · outbound

This paper cites Lee, and Mengdi Wang.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Lee, and Mengdi Wang

Reference 6

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-08T16:11:43.109949Z digest=sha256:4caff5a1301f39edd52bdf9ea755089767a4b2816df3c1b2c87769aff1ba3a10

Observation 25e592c3-9ec2-41ac-92a0-4937fd2a9a90 · outbound

This paper cites Premise Order Matters in Reasoning with Large Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Premise Order Matters in Reasoning with Large Language Models

Reference 7

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source=arxiv_source observed=2026-08-08T16:11:43.114934Z digest=sha256:988055fae2bf196e1625a36513272798b7a89823d6a3d394f24b54d36b3ef627

Observation c1c9e37d-25a0-485d-ad36-e9cb13f2e7dc · outbound

This paper cites Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use

Reference 8

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source=arxiv_source observed=2026-08-08T16:11:43.119285Z digest=sha256:ee626f76bdaa21e5bb1bf152fb1bc355f6f45b2c6e3fa2e5c28d969d9d0db307

Observation e4392f86-f912-4bae-b7fb-a27320183020 · outbound

This paper cites Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias

Reference 9

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local_arxiv, observed 2026-08-08T16:11:58.877043Z

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source=arxiv_source observed=2026-08-08T16:11:43.123903Z digest=sha256:7a82a76b1601363de77e8a0378af246ccc56e3806b1016afebd473e32283dc0e

Observation 2cf002d3-5778-4f91-8fe2-1f8c1c7dd0f2 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-08T16:11:43.128326Z digest=sha256:1d0139e8349a2358f49f8d11ed838abfc060d6aacde5d95cf12c4a2229fc8940

Observation cdbfaff9-978e-4087-96fd-5f1321e991a6 · outbound

This paper cites Changing Answer Order Can Decrease MMLU Accuracy.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Changing Answer Order Can Decrease MMLU Accuracy

Reference 11

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source=arxiv_source observed=2026-08-08T16:11:43.132926Z digest=sha256:c271ffd8a5b235a4a1579c49c060233795a13911f04b887d9b1c0d7195894c94

Observation d41adcbb-afe8-4ebc-8d79-7d9506ff9840 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 12

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

source=arxiv_source observed=2026-08-08T16:11:43.137419Z digest=sha256:402968de33fd67c830aff9d572ab3fa1556bba56521ed713cb10848796a9ef31

Observation 50ff3154-c93e-41b1-81fe-ac5201bccfb2 · outbound

This paper cites Transformer Language Models without Positional Encodings Still Learn Positional Information.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Transformer Language Models without Positional Encodings Still Learn Positional Information

Reference 13

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source=arxiv_source observed=2026-08-08T16:11:43.141542Z digest=sha256:e08b57f71e6a6350c94811c19a5265660ef7906cbab40ba5e4a77ce856ba431f

Observation 26d74a18-5701-416b-92eb-431831c8d5bd · outbound

This paper cites Measuring Massive Multitask Language Understanding.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Measuring Massive Multitask Language Understanding

Reference 14

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source=arxiv_source observed=2026-08-08T16:11:43.145904Z digest=sha256:5e91497f6886536cf5e0b28d5ecf6236f4f930e31e745077f44c8c41896fad51

Observation 20482803-7ae3-4de3-8af2-e4ec0590e16c · outbound

This paper cites Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Reference 15

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source=arxiv_source observed=2026-08-08T16:11:43.150444Z digest=sha256:83f4dd9592439cf30ead6d917e4088bdb1b357fc9c54a1ee238bacaef4681eb2

Observation ee0cbb3f-a312-4ffa-84bb-23bf533c3c64 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 16

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source=arxiv_source observed=2026-08-08T16:11:43.154816Z digest=sha256:b38f9715e087d8e6eb74ffc3c52b7063e334568985a5ee9459e6a573fc2c25ba

Observation d8d80f79-dc2c-424c-b3ee-06860d3d8294 · outbound

This paper cites Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training

Reference 17

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source=arxiv_source observed=2026-08-08T16:11:43.159126Z digest=sha256:77a11de09c2984974082bae14cf4bf053109eb136693c48fb5163ba81c0fdf55

Observation 64f84c4e-52ab-4032-bfff-e98d15b56290 · outbound

This paper cites Kosiorek, Seungjin Choi, and Yee Whye Teh.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Kosiorek, Seungjin Choi, and Yee Whye Teh

Reference 18

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

source=arxiv_source observed=2026-08-08T16:11:43.163493Z digest=sha256:aa656b52ce9b2d7a3489c3e4a599e8e8a12ffaede8e457f17aaf34dc7913824c

Observation 2d73777d-18d7-4767-8359-7ca070f796f4 · outbound

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RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-08T16:11:43.167878Z digest=sha256:b3ddfa3b06ed55b1f44e314c00eb1124f30bb629c60bc348344657ca4e863695

Observation 602bd77f-2fee-4c0d-a613-50ecfbde5380 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 20

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source=arxiv_source observed=2026-08-08T16:11:43.172010Z digest=sha256:4a3324fc4a64e028581f3b84883ac147efbe0baebf313eccdd01b8cdd27b9849

Observation 86719eb5-49df-46f1-9c24-1327797a6b00 · outbound

This paper cites LooGLE: Can Long-Context Language Models Understand Long Contexts?.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs LooGLE: Can Long-Context Language Models Understand Long Contexts?

Reference 21

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source=arxiv_source observed=2026-08-08T16:11:43.176176Z digest=sha256:254abbf9a815f89bd7ccd88bd702870c3dd2bc30bec555b1847982b1ef459f8f

Observation d0b566b4-f2a2-4911-b5b1-57ebc1451f57 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-08-08T16:11:43.180292Z digest=sha256:7dc5a58233600e9f771ee373e2410278e3df263398a4013d8b99a1777f62976a

Observation a0947c20-6a1b-469e-9874-6a1b9ab3e20b · outbound

This paper cites Order-Independence Without Fine Tuning.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Order-Independence Without Fine Tuning

Reference 23

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local_arxiv, observed 2026-08-08T16:11:43.666537Z

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

source=arxiv_source observed=2026-08-08T16:11:43.184424Z digest=sha256:f3fa1f536c031a65684e0fa15e29097cf1b2e5f9e773296e5b296769b9db6ce9

Observation 7afbb6e7-300e-4ca3-b989-75e55d2b9314 · outbound

This paper cites Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs

Reference 24

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source=arxiv_source observed=2026-08-08T16:11:43.188844Z digest=sha256:7d6a9f706ef4e75fb07ecbcfe0c90a5937dfa977ceae1618d802ae0298152193

Observation a612b12d-8eb9-414f-9b18-3d03bd011cda · outbound

This paper cites Relational Pooling for Graph Representations.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Relational Pooling for Graph Representations

Reference 25

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source=arxiv_source observed=2026-08-08T16:11:43.193305Z digest=sha256:8a8a9ace89088067518efbdb3fb958f232cc7c229083e137186e625e8267aa22

Observation c5e32d7c-4704-4dd7-bb48-ba676604af5e · outbound

This paper cites Document Ranking with a Pretrained Sequence-to-Sequence Model.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Document Ranking with a Pretrained Sequence-to-Sequence Model

Reference 26

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source=arxiv_source observed=2026-08-08T16:11:43.197790Z digest=sha256:532a9143570c0e350df2cbe0630df5a01effa07fadbbe5e62b2c338493018e07

Observation af62373e-cc99-4318-b692-62ed7484d3f7 · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs YaRN: Efficient Context Window Extension of Large Language Models

Reference 27

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source=arxiv_source observed=2026-08-08T16:11:43.202396Z digest=sha256:c2c1c27214f469239e1fe2bd8c6f73cd3690480a5830dce28c6a1377552c745a

Observation 6a2e0a31-f178-4042-b9b2-62f72cffa428 · outbound

This paper cites Attention Sorting Combats Recency Bias In Long Context Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Attention Sorting Combats Recency Bias In Long Context Language Models

Reference 28

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source=arxiv_source observed=2026-08-08T16:11:43.207103Z digest=sha256:2d222cd0d472d7167100e852d7e4916f429b24610a508cd4131ab25fd8870eee

Observation 27005861-b8a7-47f6-be9c-df46ef330059 · outbound

This paper cites Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions

Reference 29

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source=arxiv_source observed=2026-08-08T16:11:43.211750Z digest=sha256:c998790cc26f46a8047c1be8768902151d055e270977ed7b6436d6888f761dc3

Observation 4c814db6-eae6-4497-ae8c-3012900dd8c4 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 30

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source=arxiv_source observed=2026-08-08T16:11:43.216366Z digest=sha256:ec913a4e45ce457d773d32cc0a1ab9d9ed08a5f32cab16a4f61c3509cf418717

Observation 816a7cdc-4a43-498b-b502-10e21a8700bc · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 31

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

source=arxiv_source observed=2026-08-08T16:11:43.222006Z digest=sha256:8dddf7ae72b95d70658f5bbc2bf2124a4703fc45f7d93e64f20cbbb54535d0b5

Observation f049aa28-2781-41e6-81db-1cb369399da5 · outbound

This paper cites MPNet: Masked and Permuted Pre-training for Language Understanding.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs MPNet: Masked and Permuted Pre-training for Language Understanding

Reference 32

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source=arxiv_source observed=2026-08-08T16:11:43.227340Z digest=sha256:cace4a135ac1d33fce630dc9ad107a6119eea7a7bcc23c16ef23d18691cf41b6

Observation 7b2db6a0-f6e5-4046-87a7-c23e8b169f1d · outbound

This paper cites Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models

Reference 33

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source=arxiv_source observed=2026-08-08T16:11:43.231800Z digest=sha256:09098580078e9e45f454e6894fb882c13fe2d59369d013329036dfcc8969e232

Observation 08cb476e-0d86-4263-95f2-f81087b6417d · outbound

This paper cites Eliminating Position Bias of Language Models: A Mechanistic Approach.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Eliminating Position Bias of Language Models: A Mechanistic Approach

Reference 34

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source=arxiv_source observed=2026-08-08T16:11:43.236300Z digest=sha256:2456abeef4d1e716d0add8acefc0fb2e52ffe48f2a9bdaeeb8638f3d21870447

Observation e6cc40c6-2a0f-4678-818b-98a9e35f26ba · outbound

This paper cites Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models

Reference 35

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source=arxiv_source observed=2026-08-08T16:11:43.241022Z digest=sha256:e076c1f33161eae9df0928153b6c29859385d20c414172208e95e26f5799047c

Observation 93bb9818-0472-433d-8dd5-b8dd179da69b · outbound

This paper cites Retrieval meets Long Context Large Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Retrieval meets Long Context Large Language Models

Reference 36

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source=arxiv_source observed=2026-08-08T16:11:43.245455Z digest=sha256:a5fef077e598715008b0fe98c5eb5dfdff99f0e7d5fd3d7fb6b575d9e3a000dd

Observation f04a5292-11e3-4358-8bea-9ab0a1a3549c · outbound

This paper cites TableFormer: Robust Transformer Modeling for Table-Text Encoding.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs TableFormer: Robust Transformer Modeling for Table-Text Encoding

Reference 37

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Source-reported events for the cited work

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Observation 4fbc86f6-2a09-436e-b3d0-36dcd4e0b1d2 · outbound

This paper cites Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration

Reference 38

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verified exact
local_arxiv, observed 2026-08-08T16:11:43.475433Z

Source-reported events for the cited work

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

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Observation b6a56d67-c6d5-4008-a1be-e239b1593f1a · outbound

This paper cites Long-Context Language Modeling with Parallel Context Encoding.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Long-Context Language Modeling with Parallel Context Encoding

Reference 39

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no resolver link, observed 2026-08-08T16:11:43.258903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:11:43.258903Z digest=sha256:97c873109da46da006632621ce68ee231cae279674a263ae3568d5c547d07756

Observation f6b33c8d-d6d3-4a1c-ae68-bd913ba2e50a · outbound

This paper cites Mitigate Position Bias in Large Language Models via Scaling a Single Dimension.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Mitigate Position Bias in Large Language Models via Scaling a Single Dimension

Reference 40

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no resolver link, observed 2026-08-08T16:11:43.263677Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-08T16:11:43.263677Z digest=sha256:f7efc3563fcdf5d69cf4b468c408f21c599ac77ed96fd53c092919e63e62f6e0

Observation 83637988-97db-49b3-9823-231e5813fd6e · outbound

This paper cites Marathon: A Race Through the Realm of Long Context with Large Language Models.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Marathon: A Race Through the Realm of Long Context with Large Language Models

Reference 41

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no resolver link, observed 2026-08-08T16:11:43.267926Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-08T16:11:43.267926Z digest=sha256:4c483e91b9b2446366d12c5058a821b01b7567c8f4f47955b728b19b27e2258e

Observation a5cddc6b-4d55-445b-b55f-3672afa053fb · outbound

This paper cites Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-08T16:11:43.273346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:11:43.273346Z digest=sha256:8457b42e21f84888749a7e0c40d4f21632d15182fc935fe3c1d7a85a7bb08782

Observation 6ce328d6-30c5-4198-8fc8-b9ed21794008 · outbound

This paper cites RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations

Reference 43

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unresolved
no resolver link, observed 2026-08-08T16:11:43.277982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:11:43.277982Z digest=sha256:4c77c6e8f7d3462a628fa60b102b4da12ecd7ed6e1985b3fb90dee322d248098

Observation 3d638b7b-b9d6-4b2c-898d-178b9c120022 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 44

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unresolved
raw_fallback, observed 2026-08-08T16:11:59.011296Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-08T16:11:43.283276Z digest=sha256:b4f105e3061c0613478f10b24651672bd024154dcebc068ceaa632ea6b4bca3f

Observation c1a5f3c6-2c83-4c46-9ce4-e545f8dc00d1 · outbound

This paper cites an unresolved cited work.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs Unresolved cited work

Reference 45

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unresolved
raw_fallback, observed 2026-08-08T16:11:58.996620Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-08T16:11:43.287797Z digest=sha256:9e922df64ed181e7f4114be681a61a1eadc8a8f8ab8eaff9d5f87c53a64a0339

Observation 07e90783-269e-41a3-9134-acecebb3b2e4 · outbound

This paper cites FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-08T16:11:43.384909Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-08T16:11:43.292066Z digest=sha256:be92ec049f537fdb0fa9e482751c13536d28acb9ade28f59bf1b5538a625c401

Observation 0ee9611e-05e1-43cf-a692-2d2cc14ca5f8 · outbound

This paper cites JudgeLM: Fine-tuned Large Language Models are Scalable Judges.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs JudgeLM: Fine-tuned Large Language Models are Scalable Judges

Reference 47

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unresolved
no resolver link, observed 2026-08-08T16:11:43.296725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:11:43.296725Z digest=sha256:228f90cdf1fbd1f23a258fe8a81880a98be9b0487efa57d8aa52f3dca6519ac3

Observation 9604083b-9e7f-4bb8-93c7-264383d4ce09 · outbound

This paper cites online" 'onlinestring :=.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs online" 'onlinestring :=

Reference 48

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no resolver link, observed 2026-08-08T16:11:43.301560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:11:43.301560Z digest=sha256:ad5ea393cc17874407d68ae1bcc15d4eb6304865ae1fd4c43f03ec3c6496af6d

Observation 97957719-3740-4347-ae55-4336663d121c · outbound

This paper cites write newline.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs write newline

Reference 49

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unresolved
no resolver link, observed 2026-08-08T16:11:43.306809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:11:43.306809Z digest=sha256:2e00e4f07fae8ca6e9dc14a13f50505774f77629013c61cfd355f160fc226692

Pith citing papers

No inbound Pith citation observations are available.