{"work":{"id":"45b326e2-e962-41a5-a542-2559e103a19b","openalex_id":null,"doi":null,"arxiv_id":"2310.01405","raw_key":null,"title":"Representation Engineering: A Top-Down Approach to AI Transparency","authors":null,"authors_text":"Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren","year":2023,"venue":"cs.LG","abstract":"In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.","external_url":"https://arxiv.org/abs/2310.01405","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-14T22:19:31.714704+00:00","pith_arxiv_id":"2310.01405","created_at":"2026-05-08T21:29:13.619448+00:00","updated_at":"2026-05-14T22:19:31.714704+00:00","title_quality_ok":true,"display_title":"Representation Engineering: A Top-Down Approach to AI Transparency","render_title":"Representation Engineering: A Top-Down Approach to AI Transparency"},"hub":{"state":{"work_id":"45b326e2-e962-41a5-a542-2559e103a19b","tier":"super_hub","tier_reason":"100+ Pith inbound or 10,000+ external citations","pith_inbound_count":103,"external_cited_by_count":null,"distinct_field_count":11,"first_pith_cited_at":"2023-11-07T01:59:11+00:00","last_pith_cited_at":"2026-05-13T16:48:48+00:00","author_build_status":"needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-05-14T23:06:18.264629+00:00","tier_text":"super_hub"},"tier":"super_hub","role_counts":[{"context_role":"background","n":1}],"polarity_counts":[{"context_polarity":"unclear","n":1}],"runs":{"ask_index":{"job_type":"ask_index","status":"succeeded","result":{"title":"Representation Engineering: A Top-Down Approach to AI Transparency","claims":[{"claim_text":"In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and con","claim_type":"abstract","evidence_strength":"source_metadata"}],"why_cited":"Pith tracks Representation Engineering: A Top-Down Approach to AI Transparency because it crossed a citation-hub threshold.","role_counts":[]},"error":null,"updated_at":"2026-05-14T20:26:25.492190+00:00"},"author_expand":{"job_type":"author_expand","status":"succeeded","result":{"authors_linked":[{"id":"d5f98311-adb1-4b25-880e-4f7dd5576ee8","orcid":null,"display_name":"Andy Zou"},{"id":"72a7e9fa-3998-4424-a7d6-e43a696a91d5","orcid":null,"display_name":"Long Phan"},{"id":"43252ad7-ee42-4f0d-99ae-5bf6c12a1563","orcid":null,"display_name":"Sarah Chen"},{"id":"3dfd48ff-ea4c-49fc-a4b6-eec72facd5ff","orcid":null,"display_name":"James Campbell"},{"id":"0aadf049-5e35-4dd1-94eb-1f65a07eccd6","orcid":null,"display_name":"Phillip Guo"},{"id":"fa736aef-cec8-4cd4-a310-d04ec0231660","orcid":null,"display_name":"Richard Ren"}]},"error":null,"updated_at":"2026-05-14T20:26:28.605187+00:00"},"context_extract":{"job_type":"context_extract","status":"succeeded","result":{"enqueued_papers":25},"error":null,"updated_at":"2026-05-14T05:56:39.141479+00:00"},"graph_features":{"job_type":"graph_features","status":"succeeded","result":{"co_cited":[{"title":"Steering Language Models With Activation Engineering","work_id":"d525fe06-5560-4e97-86fc-7a0e551f5b17","shared_citers":44},{"title":"The Llama 3 Herd of Models","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","shared_citers":27},{"title":"Qwen3 Technical Report","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","shared_citers":23},{"title":"Qwen2.5 Technical Report","work_id":"d8432992-4980-4a81-85c7-9fa2c2b87f85","shared_citers":18},{"title":"The Linear Representation Hypothesis and the Geometry of Large Language Models","work_id":"a7b44adc-f2c2-4420-a27d-8ade97dd3b75","shared_citers":17},{"title":"Persona Vectors: Monitoring and Controlling Character Traits in Language Models","work_id":"cf32dbef-9132-4648-abcb-0ebf3ac3af80","shared_citers":16},{"title":"Steering Llama 2 via Contrastive Activation Addition","work_id":"3317feaa-e788-45fc-95aa-4ea20028b55b","shared_citers":16},{"title":"Training Verifiers to Solve Math Word Problems","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","shared_citers":14},{"title":"The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets","work_id":"400e017f-8643-4166-b6da-a75d4446da80","shared_citers":13},{"title":"Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations","work_id":"93844332-869b-448c-a1be-35466150b1b2","shared_citers":12},{"title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","work_id":"68a5177f-d644-44c1-bd4f-4e5278c22f5d","shared_citers":11},{"title":"Toy Models of Superposition","work_id":"43875dbe-bc2d-4ab5-af63-744411533ff7","shared_citers":11},{"title":"Eliciting Latent Predictions from Transformers with the Tuned Lens","work_id":"a127314f-7424-488f-b6d7-8214650c420f","shared_citers":10},{"title":"Mistral 7B","work_id":"eb5e1305-ad11-4875-ad8d-ad8b8f697599","shared_citers":10},{"title":"Sparse Autoencoders Find Highly Interpretable Features in Language Models","work_id":"51960d72-c69f-4db8-8efd-e90e8b4d9524","shared_citers":10},{"title":"Steering llama 2 via contrastive activation addition","work_id":"1c681d32-40e4-4e00-b5a1-ddec8430f6ca","shared_citers":10},{"title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","work_id":"3322fa86-1768-4677-8425-dd326b45e078","shared_citers":10},{"title":"Linear representations of sentiment in large language models","work_id":"6cb3c7a7-3301-449f-97b9-7e047edafdf9","shared_citers":9},{"title":"Refusal in Language Models Is Mediated by a Single Direction","work_id":"fbb9538d-8e58-4902-9fbd-b11f044bc2d5","shared_citers":9},{"title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","work_id":"a1f2574b-a899-4713-be60-c87ba332656c","shared_citers":9},{"title":"Understanding intermediate layers using linear classifier probes","work_id":"bdc944db-4be2-44f7-950b-eaef12fab00e","shared_citers":9},{"title":"Gemma 2: Improving Open Language Models at a Practical Size","work_id":"4dd94e2f-2b27-4cbf-88a0-4910f0772a57","shared_citers":8},{"title":"Constitutional AI: Harmlessness from AI Feedback","work_id":"faaaa4e0-2676-4fac-a0b4-99aef10d2095","shared_citers":7},{"title":"GPT-4 Technical Report","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","shared_citers":7}],"time_series":[{"n":2,"year":2023},{"n":2,"year":2024},{"n":89,"year":2026}],"dependency_candidates":[]},"error":null,"updated_at":"2026-05-14T05:56:39.183848+00:00"},"identity_refresh":{"job_type":"identity_refresh","status":"succeeded","result":{"items":[{"title":"Qwen3 Technical Report","outcome":"unchanged","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","resolver":"local_arxiv","confidence":0.98,"old_work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e"}],"counts":{"fixed":0,"merged":0,"unchanged":1,"quarantined":0,"needs_external_resolution":0},"errors":[],"attempted":1},"error":null,"updated_at":"2026-05-14T05:56:41.875790+00:00"},"role_polarity":{"job_type":"role_polarity","status":"succeeded","result":{"title":"Representation Engineering: A Top-Down Approach to AI Transparency","claims":[{"claim_text":"In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and con","claim_type":"abstract","evidence_strength":"source_metadata"}],"why_cited":"Pith tracks Representation Engineering: A Top-Down Approach to AI Transparency because it crossed a citation-hub threshold.","role_counts":[]},"error":null,"updated_at":"2026-05-14T20:26:33.115173+00:00"},"summary_claims":{"job_type":"summary_claims","status":"succeeded","result":{"title":"Representation Engineering: A Top-Down Approach to AI Transparency","claims":[{"claim_text":"In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and con","claim_type":"abstract","evidence_strength":"source_metadata"}],"why_cited":"Pith tracks Representation Engineering: A Top-Down Approach to AI Transparency because it crossed a citation-hub threshold.","role_counts":[]},"error":null,"updated_at":"2026-05-14T05:56:44.378914+00:00"}},"summary":{"title":"Representation Engineering: A Top-Down Approach to AI Transparency","claims":[{"claim_text":"In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and con","claim_type":"abstract","evidence_strength":"source_metadata"}],"why_cited":"Pith tracks Representation Engineering: A Top-Down Approach to AI Transparency because it crossed a citation-hub threshold.","role_counts":[]},"graph":{"co_cited":[{"title":"Steering Language Models With Activation Engineering","work_id":"d525fe06-5560-4e97-86fc-7a0e551f5b17","shared_citers":44},{"title":"The Llama 3 Herd of Models","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","shared_citers":27},{"title":"Qwen3 Technical Report","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","shared_citers":23},{"title":"Qwen2.5 Technical Report","work_id":"d8432992-4980-4a81-85c7-9fa2c2b87f85","shared_citers":18},{"title":"The Linear Representation Hypothesis and the Geometry of Large Language Models","work_id":"a7b44adc-f2c2-4420-a27d-8ade97dd3b75","shared_citers":17},{"title":"Persona Vectors: Monitoring and Controlling Character Traits in Language Models","work_id":"cf32dbef-9132-4648-abcb-0ebf3ac3af80","shared_citers":16},{"title":"Steering Llama 2 via Contrastive Activation Addition","work_id":"3317feaa-e788-45fc-95aa-4ea20028b55b","shared_citers":16},{"title":"Training Verifiers to Solve Math Word Problems","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","shared_citers":14},{"title":"The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets","work_id":"400e017f-8643-4166-b6da-a75d4446da80","shared_citers":13},{"title":"Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations","work_id":"93844332-869b-448c-a1be-35466150b1b2","shared_citers":12},{"title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","work_id":"68a5177f-d644-44c1-bd4f-4e5278c22f5d","shared_citers":11},{"title":"Toy Models of Superposition","work_id":"43875dbe-bc2d-4ab5-af63-744411533ff7","shared_citers":11},{"title":"Eliciting Latent Predictions from Transformers with the Tuned Lens","work_id":"a127314f-7424-488f-b6d7-8214650c420f","shared_citers":10},{"title":"Mistral 7B","work_id":"eb5e1305-ad11-4875-ad8d-ad8b8f697599","shared_citers":10},{"title":"Sparse Autoencoders Find Highly Interpretable Features in Language Models","work_id":"51960d72-c69f-4db8-8efd-e90e8b4d9524","shared_citers":10},{"title":"Steering llama 2 via contrastive activation addition","work_id":"1c681d32-40e4-4e00-b5a1-ddec8430f6ca","shared_citers":10},{"title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","work_id":"3322fa86-1768-4677-8425-dd326b45e078","shared_citers":10},{"title":"Linear representations of sentiment in large language models","work_id":"6cb3c7a7-3301-449f-97b9-7e047edafdf9","shared_citers":9},{"title":"Refusal in Language Models Is Mediated by a Single Direction","work_id":"fbb9538d-8e58-4902-9fbd-b11f044bc2d5","shared_citers":9},{"title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","work_id":"a1f2574b-a899-4713-be60-c87ba332656c","shared_citers":9},{"title":"Understanding intermediate layers using linear classifier probes","work_id":"bdc944db-4be2-44f7-950b-eaef12fab00e","shared_citers":9},{"title":"Gemma 2: Improving Open Language Models at a Practical Size","work_id":"4dd94e2f-2b27-4cbf-88a0-4910f0772a57","shared_citers":8},{"title":"Constitutional AI: Harmlessness from AI Feedback","work_id":"faaaa4e0-2676-4fac-a0b4-99aef10d2095","shared_citers":7},{"title":"GPT-4 Technical Report","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","shared_citers":7}],"time_series":[{"n":2,"year":2023},{"n":2,"year":2024},{"n":89,"year":2026}],"dependency_candidates":[]},"authors":[{"id":"d5f98311-adb1-4b25-880e-4f7dd5576ee8","orcid":null,"display_name":"Andy Zou","source":"manual","import_confidence":0.72},{"id":"3dfd48ff-ea4c-49fc-a4b6-eec72facd5ff","orcid":null,"display_name":"James Campbell","source":"manual","import_confidence":0.72},{"id":"72a7e9fa-3998-4424-a7d6-e43a696a91d5","orcid":null,"display_name":"Long Phan","source":"manual","import_confidence":0.72},{"id":"0aadf049-5e35-4dd1-94eb-1f65a07eccd6","orcid":null,"display_name":"Phillip Guo","source":"manual","import_confidence":0.72},{"id":"fa736aef-cec8-4cd4-a310-d04ec0231660","orcid":null,"display_name":"Richard Ren","source":"manual","import_confidence":0.72},{"id":"43252ad7-ee42-4f0d-99ae-5bf6c12a1563","orcid":null,"display_name":"Sarah Chen","source":"manual","import_confidence":0.72}]}}