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5.1 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.8 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.6 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.3 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src1.4 Perceptual Reality Transformer Explores What Illustrations Must Preserve — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.1 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.8 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.6 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.3 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src1.4 Perceptual Reality Transformer Explores What Illustrations Must Preserve — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
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recommender-systems

topic10 events
papersTODAY 04:00 UTC

Paper Fine-Tunes LLM Recommender to Explain Its Suggestions Safely

A new arXiv preprint proposes treating safety as a constraint when fine-tuning a large language model used as a recommender system. Standard recommenders are trained only to predict the next item a user will engage with, not to justify the prediction, so the authors add self-explanation as a training objective. The goal is to give users personalized reasons for suggestions without letting the generated explanations violate safety requirements.

recommender-systems/fine-tuning/safety/explainability
papersTODAY 04:00 UTC

CRAMER: Request-Aware Masking Method for Editing Sequential Recommenders

A revised arXiv paper introduces CRAMER, a technique for controlling sequential recommendation models through request-aware masking. The approach aims to let such models respond more flexibly to users' immediate, time-sensitive requests rather than only their long-term behavior patterns. The paper appears on arXiv's cs.AI and cs.LG listings as a cross-list replacement.

recommender-systems/arxiv/masking/personalization
papersTODAY 04:00 UTC

arXiv paper proposes Thompson sampling method for multi-objective public media decisions

A revised arXiv paper introduces a contextual scalarisation Thompson sampling approach for recommender systems that must balance several competing goals at once. The authors use public service media editorial decisions as a motivating case, where audience reach, cultural values, public service duties, and operational limits all compete. The method aims to support decision-making when objectives cannot be easily reduced to a single score.

recommender-systems/multi-objective/research/public-media
papersTODAY 04:00 UTC

arXiv paper proposes entropy-routed mixture of experts for multimodal recommendation

A revised arXiv preprint introduces a multimodal recommendation method that combines collaborative signals with visual and textual item features. It uses modality-guided mixture of structured experts, where entropy-based routing decides how much to rely on each evidence source per user-item interaction. Diagnostic probes trained on individual modalities are used to partition held-out interactions and guide the routing behavior.

multimodal/recommender-systems/mixture-of-experts/arxiv
papersSEP 12 04:00 UTC

Study Examines Regularization Effects in Linear Recommendation Models

A new arXiv preprint analyzes how regularization shapes the behavior of linear recommendation models. The work situates these simpler models against deep-learning-inspired recommenders that currently lead on standard recommendation benchmarks. It appears aimed at clarifying when and why regularization choices matter for ranking performance.

recommender-systems/regularization/arxiv/benchmarks
papersSEP 10 04:00 UTC

Paper proposes personalized execution time optimization for billion-scale scheduled jobs

A research paper presents a method for timing scheduled batch jobs on large asynchronous computing platforms, where tasks such as promotional notifications and recommender pre-computation must run at the right moment. The approach personalizes execution times across very large job volumes, aiming to improve how promptly information is delivered or refreshed.

scheduling/recommender-systems/large-scale/batch-jobs
papersSEP 10 04:00 UTC

Study Recovers Expert-Based Artist Similarity Networks from Audio for Music Recommendation

A machine learning paper introduces a method that infers adjacency links between musical artists from the acoustic distributions of their recordings, treating expert critic judgments as a reference standard. The authors position this as a third recommendation signal beyond user interaction data, which struggles in cold-start settings, and intrinsic audio content alone. A construct-validity framework is used to assess whether the recovered networks meaningfully reflect artist similarity.

music-recommendation/cold-start/acoustic-analysis/validation
papersSEP 10 04:00 UTC

arXiv Paper Proposes Gamified 20Q Recommender for Cybersecurity Education

A revised arXiv paper cross-listed in AI and machine learning presents a gamified recommender that uses a 20-questions style interaction to support cybersecurity education. The authors argue that traditional security training often leaves learners disengaged and position the game-based tool as a more explanatory alternative as threats grow more sophisticated. The v2 entry appears in both the cs.AI and cs.LG listings.

cybersecurity/education/gamification/recommender-systems
papersSEP 10 04:00 UTC

Researchers Detail Agentic Group Shilling Attack Method Targeting Recommender Systems

A new arXiv paper presents a coordinated multi-agent approach for manipulating recommender systems by simulating user behavior to steer ranking outcomes. The authors report that the method can influence recommendations effectively while keeping resource costs manageable. The work highlights a security concern for platforms that depend on user-interaction data to personalize content and purchases.

agents/recommender-systems/security/research