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Paper Citation Record · LEDGER

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference

As of 15 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2607.18259.

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

pith.paper-citation-record.v1
2607.18259 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:56:50.725313Z

measured 70 of 70 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

70 of 70 outbound references displayed

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Outbound references

Observation 3457eb35-7fbf-4462-9490-a4f5595084dd · outbound

This paper cites 2026 , eprint=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference 2026 , eprint=

Reference 4

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Observation 32d8757d-6b3d-4720-8695-7d67836eb188 · outbound

This paper cites 2026 , eprint=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference 2026 , eprint=

Reference 5

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Observation bbe4f0d6-74b4-4f46-8e5d-1990ffa0afc4 · outbound

This paper cites Neurocomputing , volume=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Neurocomputing , volume=

Reference 7

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Observation 4e412589-1997-49c7-baf7-93d8ad0b0f81 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in Neural Information Processing Systems , volume=

Reference 8

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Observation 3baa9c7f-6e58-4f90-a39d-80d7bb6d1004 · outbound

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

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 10

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Observation a069dd5b-e81d-43ee-98da-481c9912a8e4 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 12

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This paper cites Findings of the association for computational linguistics: ACL 2023 , pages=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Findings of the association for computational linguistics: ACL 2023 , pages=

Reference 13

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Observation b19fd105-03af-4549-9ea8-2a18c7fef05e · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in Neural Information Processing Systems , volume=

Reference 14

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Observation 98d939de-7df7-428a-8e95-b51ea8c6d9f5 · outbound

This paper cites Advances in neural information processing systems , volume=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in neural information processing systems , volume=

Reference 22

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Observation 439ec5f2-103d-4d50-9a4e-c2ddca0b46a7 · outbound

This paper cites Extending Activation Steering to Broad Skills and Multiple Behaviours.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Extending Activation Steering to Broad Skills and Multiple Behaviours

Reference 23

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference The 2023 Conference on Empirical Methods in Natural Language Processing , year=

Reference 24

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Observation 4392138e-5221-478f-8575-3cc88d49c604 · outbound

This paper cites ICLR 2025 Workshop on Building Trust in Language Models and Applications , year=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference ICLR 2025 Workshop on Building Trust in Language Models and Applications , year=

Reference 25

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Observation d23fec36-d839-4a97-8805-f2f353cc68d2 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 27

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Observation 9e503b0e-e0b5-495f-a9ee-d761d2941305 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 28

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Observation 940f1330-5b5b-425d-83c9-0135ab918dca · outbound

This paper cites In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering

Reference 30

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Observation 36c92783-41c3-472d-8aa1-96321411b109 · outbound

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference , author=

Reference 31

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Observation 3d2b44ed-17ab-46d9-bdbf-afeb498a85cf · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in Neural Information Processing Systems , volume=

Reference 32

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Observation a4222966-a5c9-4008-9033-d8fcac148583 · outbound

This paper cites Proceedings of the ACM on Web Conference 2025 , pages=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Proceedings of the ACM on Web Conference 2025 , pages=

Reference 36

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Observation 71d3ad5d-1205-48ce-8f08-21638e79ba52 · outbound

This paper cites Extracting Latent Steering Vectors from Pretrained Language Models.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Extracting Latent Steering Vectors from Pretrained Language Models

Reference 38

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Observation 3d6bf428-3309-457d-a9e5-98867ef7f4d7 · outbound

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

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Representation Engineering: A Top-Down Approach to AI Transparency

Reference 39

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Observation e3ffc76d-23d5-4643-8154-136a4863b8ad · outbound

This paper cites The Foundations of Tokenization: Statistical and Computational Concerns.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference The Foundations of Tokenization: Statistical and Computational Concerns

Reference 40

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Observation ae86c2dd-44ca-4d61-bc2d-181c4cf39c9f · outbound

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference European conference on computer vision , pages=

Reference 43

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Observation 0fdd9146-8ff6-40f8-a1ea-48773766e1a9 · outbound

This paper cites Discovering Language Model Behaviors with Model-Written Evaluations.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Discovering Language Model Behaviors with Model-Written Evaluations

Reference 46

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Observation c5a6f162-9130-44f2-8d47-b0e28351bf35 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 47

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Observation 56aa0175-8306-4c46-9c51-06a681625ebc · outbound

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Neural computation , volume=

Reference 49

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Observation 857d1040-5cc6-43b8-96dd-56cfb539310c · outbound

This paper cites Qwen Technical Report.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Qwen Technical Report

Reference 50

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in Neural Information Processing Systems , volume=

Reference 51

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Observation 68193394-e049-4ca8-8931-198cbaec1eae · outbound

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in Neural Information Processing Systems , volume=

Reference 52

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in Neural Information Processing Systems , volume=

Reference 53

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Advances in neural information processing systems , volume=

Reference 54

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Observation b1a5844f-c30a-4f89-8b3b-435e1cde09c1 · outbound

This paper cites SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity

Reference 55

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Observation 7e1e3bcf-f60f-4d81-a747-5e965f38afee · outbound

This paper cites Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks

Reference 56

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Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Current and Future Use of Large Language Models for Knowledge Work

Reference 57

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This paper cites Personalized steering of large language models: Versatile steering vectors through bi-directional preference optimization.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Personalized steering of large language models: Versatile steering vectors through bi-directional preference optimization

Reference 58

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Observation a9b11556-ab5c-4310-8751-5d9ce434a500 · outbound

This paper cites Enhancing Fast Feed Forward Networks with Load Balancing and a Master Leaf Node.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Enhancing Fast Feed Forward Networks with Load Balancing and a Master Leaf Node

Reference 59

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Observation 7f063240-27ec-4b29-9650-cbfbfaf1b8a9 · outbound

This paper cites Truth forest: Toward multi-scale truthfulness in large language models through intervention without tuning.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Truth forest: Toward multi-scale truthfulness in large language models through intervention without tuning

Reference 60

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Observation 25b3659e-72d9-4ed6-b996-85ae59c30b97 · outbound

This paper cites Steering off Course: Reliability Challenges in Steering Language Models.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Steering off Course: Reliability Challenges in Steering Language Models

Reference 61

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Observation 6d3f5f35-f464-4007-9a96-f17ee3fcd221 · outbound

This paper cites W2s-aligntree: Weak-to-strong inference-time alignment for large language models via monte carlo tree search, 2026.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference W2s-aligntree: Weak-to-strong inference-time alignment for large language models via monte carlo tree search, 2026

Reference 62

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source=arxiv_source observed=2026-08-02T13:56:47.516999Z digest=sha256:a638abeda01b8c1b55d5582f8937f1da47da11551c15b204b78a90eed399c2ec

Observation 33529e74-26c6-4c6c-a6e7-1ca3b6d3fb99 · outbound

This paper cites How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition

Reference 63

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source=arxiv_source observed=2026-08-02T13:56:47.677932Z digest=sha256:13a55876ba2f5f776456eb8c664d6cf47f1992835d56ecc8f3c57cee0bbdd59f

Observation 36343730-8574-4536-bc14-02e2838ef3c8 · outbound

This paper cites Differentially Private Steering for Large Language Model Alignment.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Differentially Private Steering for Large Language Model Alignment

Reference 64

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source=arxiv_source observed=2026-08-02T13:56:47.814089Z digest=sha256:42c9c18785631d7cdcfa26a6e693e4c6198c9b5aa8e24014daeced0a531db819

Observation b74b9fb4-1b25-4429-b3b2-24210db92190 · outbound

This paper cites Safeswitch: Steering unsafe llm behavior via internal activation signals.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Safeswitch: Steering unsafe llm behavior via internal activation signals

Reference 65

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source=arxiv_source observed=2026-08-02T13:56:47.904841Z digest=sha256:415a8a0b42aa4dbc6a13e9f3077e394d25a651a24987a29be8b59e6cb273e6cb

Observation 9bfe7ce2-23ea-4cad-9fff-bb54a892f552 · outbound

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

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Lora: Low-rank adaptation of large language models

Reference 66

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source=arxiv_source observed=2026-08-02T13:56:47.982710Z digest=sha256:003d9e0e576e384ed9c8e3e890cf4b7b68df929403d119c2eb9dbd5ffb6eff83

Observation 8a529a3d-8a4f-45fe-9d79-0577cd4014e5 · outbound

This paper cites Toward efficient inference for mixture of experts.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Toward efficient inference for mixture of experts

Reference 67

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source=arxiv_source observed=2026-08-02T13:56:48.035482Z digest=sha256:9c54e062928d92a1e55db62581a26080a0cf5ff9386c4790d3baa258b8fa8ed9

Observation afc0539c-d60f-4e8e-a659-76ccd50bcaff · outbound

This paper cites Enhancing instruction following of llms via activation steering with dynamic rejection.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Enhancing instruction following of llms via activation steering with dynamic rejection

Reference 68

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source=arxiv_source observed=2026-08-02T13:56:48.194996Z digest=sha256:9532e66a6486f34e488e5434d34043dfa57f31745cea9be1cdd6e0008faf7e02

Observation 2d8a7c35-ce47-4fa5-8ce5-2e1bb4be1613 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Adam: A Method for Stochastic Optimization

Reference 69

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source=arxiv_source observed=2026-08-02T13:56:48.285688Z digest=sha256:5958c931aafa8f1c0f4f031f59dc54482b7ca3acd5c896db5c80d4d0e3814f51

Observation e14ff4a7-0d0d-425f-8fe5-a79caf19a97f · outbound

This paper cites Style Vectors for Steering Generative Large Language Model.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Style Vectors for Steering Generative Large Language Model

Reference 70

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source=arxiv_source observed=2026-08-02T13:56:48.420092Z digest=sha256:cc72b84bc9b20723eeb84d289eb20b6be0eff11bbad960e629f141b94a257bd8

Observation 3347296a-7b8a-4830-a1d2-e420d7259361 · outbound

This paper cites a henb \.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference a henb \

Reference 71

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source=arxiv_source observed=2026-08-02T13:56:48.564631Z digest=sha256:e31143f8f56cab84f25bb628ac1e4e234cc0c16164aedfbfec7efeef126946b0

Observation 1d590ec7-7016-44ac-9553-ee9fa204e8ab · outbound

This paper cites Contextualization Distillation from Large Language Model for Knowledge Graph Completion.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Contextualization Distillation from Large Language Model for Knowledge Graph Completion

Reference 72

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source=arxiv_source observed=2026-08-02T13:56:48.689173Z digest=sha256:7810cd587ce0bc086cad2a7703798e4999cb99fc7a585590d8cfd2aa69882586

Observation f3386290-d299-42de-b7ab-c5dda9266085 · outbound

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

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Inference-time intervention: Eliciting truthful answers from a language model

Reference 73

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source=arxiv_source observed=2026-08-02T13:56:48.797887Z digest=sha256:d627582a321c362d7ddb8757ab180edf4cc910513f915d70bfa91b4d370ce222

Observation 9b032ef9-6af8-4094-8ce1-b47148b7b42f · outbound

This paper cites Understanding llms: A comprehensive overview from training to inference.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Understanding llms: A comprehensive overview from training to inference

Reference 74

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source=arxiv_source observed=2026-08-02T13:56:48.835962Z digest=sha256:3308ef63681d8b3edd5c395d3275d34e1ca25ad1b741286e5e1ca70af200f9b6

Observation 39857591-9a0e-45eb-9810-1a9a112c678a · outbound

This paper cites Steering Llama 2 via Contrastive Activation Addition.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Steering Llama 2 via Contrastive Activation Addition

Reference 75

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source=arxiv_source observed=2026-08-02T13:56:48.910319Z digest=sha256:966cfd2ac34e344007722e9b7dbe82e178873bad99a7c46dc8e902d170cddf8a

Observation afe23145-10fd-4949-ad1e-43ae8781db53 · outbound

This paper cites Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs

Reference 76

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source=arxiv_source observed=2026-08-02T13:56:49.004087Z digest=sha256:07e5cef9ae01a7e5b9ffc6d97dbcc2726b5bbbd8f0331d4a9b25a1791e5082df

Observation 6be8fc9f-0f40-4e1f-a986-3f908199a5e9 · outbound

This paper cites The Linear Representation Hypothesis and the Geometry of Large Language Models.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 77

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source=arxiv_source observed=2026-08-02T13:56:49.066703Z digest=sha256:eff2fde3e312f83b410e562dadcc3cd1a456ab8e650bc52d7a97c97de8f8d83f

Observation b38d3097-cdb6-44c6-9bb3-9cf4d21c2263 · outbound

This paper cites Discovering language model behaviors with model-written evaluations.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Discovering language model behaviors with model-written evaluations

Reference 78

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source=arxiv_source observed=2026-08-02T13:56:49.121266Z digest=sha256:639b9f0987c3d8077c059977fac950dbeb70141fe1b71449a3726be8c8b4838a

Observation 49889d5e-40a8-47fd-827a-50701475574a · outbound

This paper cites Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering

Reference 79

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source=arxiv_source observed=2026-08-02T13:56:49.249741Z digest=sha256:7e057160bb1d404d0b2742afa5cb320aca06ab0dbd807b6ee3a87aeaf0342a80

Observation 558ee70d-fecd-4003-91ea-11b5a085dde5 · outbound

This paper cites Towards Reliable Evaluation of Behavior Steering Interventions in LLMs.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Towards Reliable Evaluation of Behavior Steering Interventions in LLMs

Reference 80

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source=arxiv_source observed=2026-08-02T13:56:49.385068Z digest=sha256:375cf19874a193747f37acc3b8103e0183d4a9784d500368ad8ddd26431cfe65

Observation 528d40ef-6816-4895-832b-0cdf98f72178 · outbound

This paper cites an unresolved cited work.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Unresolved cited work

Reference 81

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source=arxiv_source observed=2026-08-02T13:56:49.493982Z digest=sha256:2c45ce357865d593aa769beccaceebf23d2122d29d9c310ea31be90f626dae03

Observation 8f4d45d0-e04e-4439-9e22-c6d1d5e84dc2 · outbound

This paper cites EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance

Reference 82

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source=arxiv_source observed=2026-08-02T13:56:49.629095Z digest=sha256:d2296b28d66f66be79bc099f5a6248e0b02a97baf135cadaec53369fc3913a72

Observation d7e0cb37-c622-4f41-876e-cea2097171f9 · outbound

This paper cites Analysing the generalisation and reliability of steering vectors.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Analysing the generalisation and reliability of steering vectors

Reference 83

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source=arxiv_source observed=2026-08-02T13:56:49.725945Z digest=sha256:73540070ff43401ffbea771f49ea59abcde5bc11a3189ee8a3672da52427aeaa

Observation bca4dc04-821d-43d2-bb9e-31b021a4d7d8 · outbound

This paper cites Steering Language Models With Activation Engineering.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Steering Language Models With Activation Engineering

Reference 84

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source=arxiv_source observed=2026-08-02T13:56:49.909558Z digest=sha256:4731ff2efbd62ac5626109ce619bc1364d86e415773c3a7ac6e8a48764878e91

Observation fcb06fcd-fcf4-44b8-bccd-5407c2667d49 · outbound

This paper cites Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective

Reference 85

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source=arxiv_source observed=2026-08-02T13:56:50.031525Z digest=sha256:5db592202a4377403bd17806ff8745ad275eb7ffa075678b154364c3e9603fd7

Observation d7d8b8d4-59f0-4794-ae80-29a3e396da7b · outbound

This paper cites Adaptive activation steering: A tuning-free llm truthfulness improvement method for diverse hallucinations categories.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Adaptive activation steering: A tuning-free llm truthfulness improvement method for diverse hallucinations categories

Reference 86

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source=arxiv_source observed=2026-08-02T13:56:50.116765Z digest=sha256:bb613579d0620c515b232e6e033c0670e493887ef10973c84db51cda36b0aedc

Observation 7cfff8b2-677f-4636-b62d-f3f9780456c8 · outbound

This paper cites Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors

Reference 87

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source=arxiv_source observed=2026-08-02T13:56:50.265683Z digest=sha256:74cc4dcabaa22049d2dc8fa564351c9adf09cfcc5b4ef12c88e24436a76f0ea5

Observation ebe1f3ff-4b8a-4ff8-88f2-ccdf43ded04b · outbound

This paper cites Controlmllm: Training-free visual prompt learning for multimodal large language models.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Controlmllm: Training-free visual prompt learning for multimodal large language models

Reference 88

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source=arxiv_source observed=2026-08-02T13:56:50.315849Z digest=sha256:8b82fbe63526662d4f0940053182c66fe4ec616f7d2a0ae79efd7c3d2b7ace60

Observation 608fb2c6-208f-4036-b5af-45253a520b6a · outbound

This paper cites AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

Reference 89

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source=arxiv_source observed=2026-08-02T13:56:50.382262Z digest=sha256:731a9a9483398360707bc331b76ab281fe7eed80643f2782a5bfdddfbfe0c6e6

Observation 12101ac5-445d-4b66-9e02-b331805b84b1 · outbound

This paper cites Improved Representation Steering for Language Models.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Improved Representation Steering for Language Models

Reference 90

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source=arxiv_source observed=2026-08-02T13:56:50.448698Z digest=sha256:31682529b6025e13e8e81e6820245664e6d552f8f500ed3ad73279a852833692

Observation 3289af1b-63f2-4110-8e6f-cbcc67b2cf18 · outbound

This paper cites Yifei, Lyle Ungar, and Jo \ a o Sedoc.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Yifei, Lyle Ungar, and Jo \ a o Sedoc

Reference 91

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source=arxiv_source observed=2026-08-02T13:56:50.505278Z digest=sha256:c1c9a8779eae1fa9ee1fcdc63317c24bcbd5f00e1d75980eaa7ee8758213082f

Observation c94fd42e-c4f8-40e2-a799-372268179875 · outbound

This paper cites Adaptable logical control for large language models.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Adaptable logical control for large language models

Reference 92

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source=arxiv_source observed=2026-08-02T13:56:50.597619Z digest=sha256:60526856e80ba185ca5f1942ca7c1bbdcd27805a401b1bf62e8e9a63588e5bdc

Observation 6ada1a97-1410-49bd-bc90-599f59b6c0f8 · outbound

This paper cites Gpt4roi: Instruction tuning large language model on region-of-interest.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Gpt4roi: Instruction tuning large language model on region-of-interest

Reference 93

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source=arxiv_source observed=2026-08-02T13:56:50.676262Z digest=sha256:5292c8e9dff61b5f5e6146203cbe36253fc5c32243db918327bcbd66d2bc55cc

Observation a3b6951e-00db-42f4-ba82-794999956623 · outbound

This paper cites Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering

Reference 94

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source=arxiv_source observed=2026-08-02T13:56:50.725313Z digest=sha256:f88b9ca8776db87bc7e5a8098e202d144ed0de39cbdcf6a75c8c711605b1b491

Pith citing papers

No inbound Pith citation observations are available.