Policy & regulation1 мин
Who was last in the digital race of Kazakhstan - Inbusiness.kz
Кто оказался последним в цифровой гонке Казахстана - Inbusiness.kz.
InBusiness.kz
Publication Catalog
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All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
2702
Stories
Research1 мин
Kazakhstan and China are preparing joint technologies for Alatau City - DKNews.kz
Казахстан и Китай готовят совместные технологии для Alatau City - DKNews.kz.
DKNews.kz
Research1 мин
An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics
arXiv:2609.10712v1 Announce Type: new Abstract: We study how model post-training and test-time inference design affect natural-language proof generation for hard olympiad mathematics. Starting from Nemotron 3 Ultra, we train two specialist checkpoints using supervised…
arXiv — cs.AI
Research1 мин
Grounding Agent Memory: Environment-Probing Curation for Enterprise Agents
arXiv:2609.11060v1 Announce Type: new Abstract: Persistent memory is entering production-oriented agent platforms to help long-horizon agents accumulate experience across sessions. Yet a post-task curator agent restricted to completed trajectories can preserve errors,…
arXiv — cs.AI
Research1 мин
A Multi-Stage Rule-Chaining Framework for Compositional and Interpretable Cognitive Reasoning
arXiv:2609.10654v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC) benchmarks cognitive generalization, the ability to infer and apply abstract rules from limited examples. This paper presents a multi-stage rule-chaining framework that performs…
arXiv — cs.AI
Research1 мин
Same Day, Same Story; One Day Ahead, a Different Signal: The Dual Validity of Financial Sentiment
arXiv:2609.11144v1 Announce Type: new Abstract: Financial NLP has a standard workflow: validate a sentiment tool against human labels, then trust it to extract market signal. This assumes the two evaluations measure the same thing. We test that assumption in a setting where both…
arXiv — cs.AI
Research1 мин
KuaiRP Series Role-playing Models Technical Report
arXiv:2609.11127v1 Announce Type: new Abstract: This paper introduces the complete technical solution for the KuaiRP series of role-playing models. We aim to achieve four core objectives for a dedicated role-playing model: simplified prompt engineering, highly stable output…
arXiv — cs.AI
Research1 мин
Finishing the Task Is Not Enough: Evaluating Agent Resilience and Considerate Participation under Accumulating Challenge
arXiv:2609.10724v1 Announce Type: new Abstract: Sustained deployment of generative AI agents requires more than isolated task success. Agents must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows, especially as…
arXiv — cs.AI
Research1 мин
Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning
arXiv:2609.10656v1 Announce Type: new Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible…
arXiv — cs.AI
Research1 мин
Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows
arXiv:2609.10964v1 Announce Type: new Abstract: Agentic LLM workflows consist of sequences of model turns interleaved with tool interactions, so their end-to-end completion time depends not only on inference speed but also on when ready turns are released. Most runtimes release…
arXiv — cs.AI
Research1 мин
Fork Where the Model Changes Its Mind: Belief-Shift Branching for Tree-Structured Reinforcement Learning
arXiv:2609.11061v1 Announce Type: new Abstract: Tree-structured rollouts give critic-free reinforcement learning with verifiable rewards (RLVR) step-level credit: fork a chain at an intermediate point, and sibling outcome differences estimate step value. Each fork adds sampling…
arXiv — cs.AI
Research1 мин
Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks
arXiv:2609.11018v1 Announce Type: new Abstract: The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research. We address this ambiguity through a survey organized around five dimensions…
arXiv — cs.AI
Research1 мин
Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
arXiv:2609.10629v1 Announce Type: new Abstract: Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired…
arXiv — cs.AI
Research1 мин
DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat
arXiv:2609.11155v1 Announce Type: new Abstract: Multi-Agent Reinforcement Learning (MARL) has emerged as a pivotal paradigm for complex decision-making in autonomous systems and air combat. While MARL has demonstrated significant potential in air combat, achieving sophisticated…
arXiv — cs.AI
Research1 мин
MOSAIC: Query-Aware Exploration Policy Adaptation for GraphRAG
arXiv:2609.11065v1 Announce Type: new Abstract: Graph Retrieval-Augmented Generation (GraphRAG) can connect evidence distributed across a corpus graph, but most systems use largely shared exploration procedures across queries. This creates a structural mismatch: direct facts may…
arXiv — cs.AI
Research1 мин
Autonomous Chemical Mechanistic Discovery through Agentic Reasoning and Validation
arXiv:2609.11147v1 Announce Type: new Abstract: Unraveling reaction mechanisms is central to modern chemistry, yet automating these investigations remains challenging because computational workflows still rely heavily on expert intervention. Here we introduce ARCHE, an…
arXiv — cs.AI
Research1 мин
The Oligarch Barely Steers Model Collapse in Multi-Model Ecosystems
arXiv:2609.11146v1 Announce Type: new Abstract: AI-generated text is flowing back into the training corpora of the next generation of models. Recursive training on it drives model collapse, and recent work extends the setting to many models feeding one another -- but almost…
arXiv — cs.AI
Research1 мин
Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking
arXiv:2609.10657v1 Announce Type: new Abstract: Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretical progress on \emph{why} this transition occurs, the quantitative structure of…
arXiv — cs.AI
Research1 мин
Probabilistic Focal Search: Accelerating Bounded-Suboptimal Search via Lower-Bound Advancement
arXiv:2609.10584v1 Announce Type: new Abstract: Bounded-suboptimal search seeks a solution within a factor $w$ of optimal while reducing search effort. Focal Search (FS) uses heuristic guidance within FOCAL, the frontier nodes eligible under the threshold $w f_{\min}$, but its…
arXiv — cs.AI
Research1 мин
Studying Without a Syllabus: Task-Agnostic Environment Preprocessing
arXiv:2609.10824v1 Announce Type: new Abstract: Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, scripts, or procedural guidance. Most automated adaptation methods, however, rely…
arXiv — cs.AI
Research1 мин
When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents
arXiv:2609.10873v1 Announce Type: new Abstract: Independent evaluation can reject harmful policy updates yet also prevent useful continual learning. We argue that update admission must be assessed through both error control and retained learning opportunities at a stated…
arXiv — cs.AI
Research1 мин
Demystifying the Privacy-Utility Trade-off in LLM Interactions
arXiv:2609.10992v1 Announce Type: new Abstract: The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods typically employ context-agnostic static rules,…
arXiv — cs.AI
Research1 мин
Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation
arXiv:2609.11115v1 Announce Type: new Abstract: Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark datasets and code, and understand the settings behind reported scores. We present…
arXiv — cs.AI
Research1 мин
Towards a Deterministic Math Solver for Clinical Language Models
arXiv:2609.10728v1 Announce Type: new Abstract: Large language models are unreliable at arithmetic, which is a problem for clinical calculators where a single numerical error changes the recommendation. The standard response is to hardcode each calculator as a validated…
arXiv — cs.AI
Research1 мин
Breaking Predictions Is Not Enough: Specified-Foil Counterfactuals for Temporal Graphs
arXiv:2609.11170v1 Announce Type: new Abstract: Temporal graph counterfactual explanations typically change past events to change or invalidate an original prediction, while leaving its replacement unspecified. Yet a user facing a predicted outcome often asks which past…
arXiv — cs.AI
Research1 мин
Debate-to-Skill: Capability-Bound Process Supervision for Industrial Query-to-Agent Annotation
arXiv:2609.11176v1 Announce Type: new Abstract: Industrial query-to-agent matching fails when topical relevance is mistaken for executable capability, especially on long-tail and boundary-sensitive requests. We formulate annotation as \emph{capability-bound process supervision}…
arXiv — cs.AI
Research1 мин
The Agent Incident Registry: Toward Preventing Repeated AI Agent Failures
arXiv:2609.11030v1 Announce Type: new Abstract: AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations. We present the Agent Incident…
arXiv — cs.AI
Research1 мин
«ИИ может убить всех нас»: разработчики нейросетей покинули Anthropic
«ИИ может убить всех нас»: разработчики нейросетей покинули Anthropic Kursiv Media
Kursiv.media — AI (Google News)
Research1 мин
OpenAI’s feud with mathematicians is only escalating
Twenty-five leading mathematicians signed an open letter arguing that AI labs are threatening their intellectual work.
TechCrunch — AI
Policy & regulation1 мин
AI is changing the agrarian science of Kazakhstan - Liter.kz
ИИ меняет аграрную науку Казахстана - Liter.kz.
Liter.kz