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Bodhan AI Releases Four Indic Models for OCR, Translation and Speech
A Hindi lesson can mix English terms (loan words), scanned tables and handwritten equations. Making that content searchable, translating it and reading it aloud requires several kinds of AI. Bodhan AI and AI4Bharat’s four new models target those jobs across Indian languages. Released in September 2026, the models cover document parsing, translation, speech recognition and speech generation, with support for mixed languages and scripts. In this [ ] The post Bodhan AI Releases Four Indic Models for OCR, Translation and Speech appeared first on Analytics Vidhya.
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RAG Is Dead as an Architecture. Retrieval Is Not.
The future of applied AI is not another retrieve-then-generate pipeline. It is deliberate context construction around a model that can Continue reading on Medium »
An Anthropic Researcher Just Resigned Publicly Over AI Extinction Fears — Here’s What’s Actually…
A viral resignation, a company insider s public agreement, and a real security incident behind the warning shot claimContinue reading on AI Tomorrow »
When Do Options Help? Policy Necrosis and Redundant Coverage in Option-Critic
arXiv:2609.05508v1 Announce Type: new Abstract: Option-critic learns options: sub-policies together with a learned rule for when each one hands control back. Its headline result is that performance improves as options are added. We explain that result, with theory and experiment. First, the termination rule option-critic learns by maximising return contributes nothing. When the termination test and the policy that picks options read the same values, the test fires at every step, so the learned rule is identical to always terminating. When that policy explores and the test does not, as in option-critic itself, the rule can block the exploration; there are instances where it suffers $\Omega(T)$ regret while always terminating holds to $O(\log T)$. Forcing termination at every step leaves the option-count curve intact. Second, the policy inside an option barely explores at all, so a state locks onto the first action that looked good and never updates again. We name this policy necrosis, g
Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Models
arXiv:2609.05437v1 Announce Type: new Abstract: Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `do not steal'). However, social intelligence depends not only on norm recognition, but also on anticipating who will enforce it and how (e.g., public shame or even imprisonment). These second-order expectations, known as metanorms, govern how people respond when social rules are broken. We introduce a novel framework for evaluating metanorm reasoning in Large Language Models (LLMs) along two dimensions: emotional appraisal and behavioral response, and propose new classification tasks, namely, predicting self-regulation in violators, and other-regulation in observers. We release a multi-perspective dataset, NormReact, of 450 norm violation scenarios, hand-annotated for emotions and behavioral responses across norm violators' gender and observers' social closeness. Current LLMs portray a har
AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning
arXiv:2609.05435v1 Announce Type: new Abstract: Modern language agents are expected to operate over long horizons: they ask follow-up questions, reuse worked examples, handle tool feedback, and adapt to delayed consequences. Most evaluations still reset the agent after a prompt or score only the final state of one trajectory. AhaBench asks a more operational question: when a fixed model receives useful experience, does its later behavior improve under a related evaluation condition where the obvious support has been removed, changed, or delayed? The suite contains three components. Aha-Puzzle tests no-hint exploration after solved hidden-state puzzles; Aha-Euler turns Project-Euler-style mathematical ideas into generated taught/held-out tasks with exact validators; and Aha-Vending, an open-source implementation inspired by Vending-Bench, tests whether a simulated vending agent remains profitable while handling delayed feedback and operational incidents. AhaBench reports a three-part sc
Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model
arXiv:2609.09213v1 Announce Type: new Abstract: We study how physical-state inputs affect a 0.8B hybrid language model adapted for manipulation with 6.2M trainable parameters. Six conditions are trained on three LIBERO-Spatial tasks and evaluated over three seeds and 540 held-out rollouts. Conditioning recurrent decay gates on geometric increments yields 28.9% success, compared with 36.7% when those increments are shuffled during training and 24.4% without explicit object/goal geometry. Both geometry policies receive correct inputs at evaluation. A token adapter using the same increments scores 27.8%; differences vary across seeds and remain inconclusive. Token-clock conditioning scores 11.1%, including one seed that fails to converge. In separate robustness tests, a state-only relative-coordinate policy retains 7/10 success under frame relabeling, whereas all four tested visual policies fall to at most 3/20 after a 5 cm object displacement. These results show no reliable advantage fro
Identifying Habit, Physics, and Nuisance in Robot World Models
arXiv:2609.09210v1 Announce Type: new Abstract: Teleoperated demonstrations are often multimodal even when the underlying dynamics are nearly deterministic given the executed action. We argue that this multimodality typically mixes three factors--operator habit in action selection, shared physics, and observation nuisance--and that entangled next-observation predictors absorb all three. We formalize the split with a structural causal model a=g(h,z,u), z'=f(z,a), o=r(z,c), and test it with complementary interventions: replacing or shuffling actions at fixed state sharply increases next-state error, whereas appearance and camera changes should not; habit-aware reverse scoring improves ranking of feasible pasts without rewriting the dynamics. The associated adaptation rule is to freeze a shared physics readout and update only a thin interface. On StackCube, DROID, and RH20T this rule improves low-shot transfer relative to training from scratch, retains cleaner dynamics under corrupted ada
CriticGen: Generation-Aware Evaluation as Actionable Feedback
arXiv:2609.05439v1 Announce Type: new Abstract: Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provide actionable feedback for model improvement. We propose CriticGen, a fine-grained, generation-aware evaluation framework that turns evaluation into actionable control for answer improvement. CriticGen first generates sample-specific evaluation dimensions and scoring criteria under high-level categories such as subjective, objective, and self-derived constraints. These criteria then serve as a dynamic rubric for jointly producing a score, a reason, an executable refinement suggestion, and a refined answer. This rubric-conditioned refinement process enables models to diagnose flaws and perform targeted answer improvement. Experimental results show that fine-grained evaluation should be both instance-specific and actionable. CriticGen induces higher-quality rubrics, improving relevance/cover
AI in Modern Software Development: How Developers Can Adapt and Drive Business Growth
AI in Modern Software Development: How Developers Can Adapt and Drive Business GrowthContinue reading on Medium »
Artificial Intelligence and AI Safety: Are We Building Systems We Can No Longer Fully Control?
We spent twenty years learning to secure software that does what it s told. Now we re shipping software that decides what to do.Continue reading on Medium »