Academic Publications: ACM Digital Library
Academic Publications · ACM Digital Library. ACM journals and conference papers. This source has 9 entries admitted under this category's review policy; each links directly to the original.
Authority tiers in this category: A2–R.
WARP: An Efficient Engine for Multi-Vector Retrieval
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies an efficient multi-vector retrieval engine. Its ACM DOI and SIGIR 2025 Best Paper Award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Retrieval and Semantic Search · Reranking
Link last checked: 2026-09-24
A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies auditing differences in chatbot service quality across dialects. Its ACM DOI and FAccT 2025 Best Paper Award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Model Evaluation and Benchmarks · Bias and Fairness
Link last checked: 2026-09-24
External Evaluation of Discrimination Mitigation Efforts in Meta's Ad Delivery
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies external evaluation of discrimination mitigations in ad delivery. Its ACM DOI and FAccT 2025 Best Paper Award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Bias and Fairness · AI Governance and Regulation
Link last checked: 2026-09-24
“You Cannot Sound Like GPT": Signs of language discrimination and resistance in computer science publishing
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies generative language technologies and linguistic discrimination in computer science publishing. Its ACM DOI and FAccT 2025 Best Paper Award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Large Language Model (LLM) · Bias and Fairness · AI Governance and Regulation
Link last checked: 2026-09-24
Shape Space Spectra
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies spectral analysis of shape spaces for continuously parameterized shape families. Its ACM DOI and SIGGRAPH 2025 Technical Papers Best Paper award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Dimensionality Reduction
Link last checked: 2026-09-24
CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies component-aligned 3D scene reconstruction from a single RGB image. Its ACM DOI and SIGGRAPH 2025 Technical Papers Best Paper award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Multimodal Models · World Models and 3D Generation
Link last checked: 2026-09-24
TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies multi-concept personalization in the token modulation space of generative models. Its ACM DOI and SIGGRAPH 2025 Technical Papers Best Paper award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Image Generation · Transformer
Link last checked: 2026-09-24
Vector-Valued Monte Carlo Integration Using Ratio Control Variates
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies vector-valued Monte Carlo integration using ratio control variates. Its ACM DOI and SIGGRAPH 2025 Technical Papers Best Paper award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Uncertainty Calibration and Selective Prediction
Link last checked: 2026-09-24
Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper studies dynamic on-body IMU calibration for inertial motion capture. Its ACM DOI and SIGGRAPH 2025 Technical Papers Best Paper award are verified, but there is currently no external expert statement showing a major contribution to AI, so it is listed only as a public entry awaiting further evidence.
Evidence use: For checking the ACM paper's identity, authors, official award and original text; it cannot currently serve as evidence of a major AI contribution confirmed by this site.
Limitations: No external statement of a major AI contribution; being listed publicly records a gap in the review and does not mean the six-step importance review was passed. The official award shows recognition under that award process; until external expert commentary is added, it cannot be claimed that the paper significantly changed AI. The technical capabilities, performance and scope in the paper are as reported by the authors; this site has not reproduced them independently.
Related concepts: Transformer
Link last checked: 2026-09-24