Centralized training with decentralized execution (CTDE) has been the dominant paradigm in multi-agent reinforcement learning (MARL), but its reliance on global state information during training introduces scalability, robustness, and generalization bottlenecks. Moreover, in practical scenarios such as adding/dropping teammates or facing environment dynamics that differ from the training, CTDE methods can be brittle and costly to retrain, whereas distributed approaches allow agents to adapt using only local information and peer-to-peer communication. We present a distributed MARL framework that […]

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Product decisions are often discussed as if they exist in a vacuum. Build the right features, optimise engagement, scale what works. In practice, those decisions are heavily shaped by context — especially in emerging markets. Shipping REM (Rapid Eye Movement), a live mobile game released to players in an emerging market, challenged many assumptions I had absorbed from mainstream game development and startup discourse. The biggest lesson was simple: good product decisions come from real constraints, not best […]

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Reliable agentic AI requires not only accurate reasoning and adaptive control, but also mechanisms that preserve reliability over time. While recent work has introduced system-level evaluation frameworks (e.g., HB-Eval) and real-time control architectures (e.g., Adapt-Plan), the question of how reliability is retained across an agent’s operational lifespan remains largely unaddressed. Existing memory mechanisms typically store experiences based on recency or salience, inadvertently allowing low-quality behaviors to accumulate and degrade long-term performance.This paper introduces Evaluation-Driven Memory (EDM), a persistence […]

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arXiv:2602.11172v1 Announce Type: new Abstract: Legal advocacy requires a unique combination of authoritative tone, rhythmic pausing for emphasis, and emotional intelligence. This study investigates the performance of the Gemini 2.5 Flash TTS and Gemini 2.5 Pro TTS models in generating synthetic courtroom speeches across five Indic languages: Tamil, Telugu, Bengali, Hindi, and Gujarati. We propose a prompting framework that utilizes Gemini 2.5s native support for 5 languages and its context-aware pacing to produce distinct advocate personas. The evolution […]

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In the past, I’ve used W&B and Tensorboard to track my experiments. They work fine for metrics, but after a few weeks, I always end up with hundreds of runs and forget why I ran half of them. I can see the configs + charts, but don’t really remember what I was trying to test. Do people just name things super carefully, track in a spreadsheet, or something else? Maybe I’m just disorganized… submitted by /u/thefuturespace [link] [comments]

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arXiv:2512.24145v2 Announce Type: replace-cross Abstract: Machine learning systems appear stochastic but are deterministically random, as seeded pseudorandom number generators produce identical realisations across repeated executions. Standard evaluation practice typically treats runs across alternatives as independent and does not exploit shared sources of randomness. This paper analyses the statistical structure of comparative evaluation under shared random seeds. Under this design, competing systems are evaluated using identical seeds, inducing matched stochastic realisations and yielding strict variance reduction whenever outcomes are […]

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arXiv:2602.00977v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in domains where errors carry high social, scientific, or safety costs. Yet standard confidence estimators, such as token likelihood, semantic similarity and multi-sample consistency, remain brittle under distribution shift, domain-specialised text, and compute limits. In this work, we present Structural Confidence, a single-pass, model-agnostic framework that enhances output correctness prediction based on multi-scale structural signals derived from a model’s final-layer hidden-state trajectory. By combining spectral, local-variation, […]

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arXiv:2601.15504v1 Announce Type: new Abstract: Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM, a lightweight spatial transcriptomics foundation model based on graph convolutional networks (GCNs) trained with a masked central spot prediction objective. Trained on 416 human Visium samples spanning 15 organs, SAGE-FM learns spatially coherent embeddings that robustly recover masked genes, with 91% of masked genes showing significant correlations (p < 0.05). The embeddings generated by […]

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Using visuals that tell a story will engage employees’ imaginations and help win their support.

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This paper introduces DEEP-PNHG (Dynamic Entity-Enhanced Personalized Headline Generation), a novel framework addressing critical challenges in personalized news headline generation, namely dynamically evolving user interests, factual consistency, and informativeness. Existing methods often struggle with these aspects. DEEP-PNHG overcomes these limitations through an innovative architecture comprising a Dynamic User Interest Graph Module, an Entity-Aware News Encoder enriched with external knowledge, and a Fact-Consistent & Personalized Joint Decoder. This design enables the generation of headlines that are simultaneously tailored to […]

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