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Latest articles, page 88

  1. Contrastive Decoding in Large Language Models: How Comparing Expert and Amateur Logits Suppresses Hallucination and Reasoning Errors

    Contrastive Decoding in Large Language Models: How Comparing Expert and Amateur Logits Suppresses Hallucination and Reasoning Errors Autoregressive large language models operate by predicting the conditional probability distribution of the next token given a sequence of preceding tokens. However, translating these continuous probability vectors into coherent, factual, and logically sound sequences remains one of the fundamental challenges of modern natural language processing. Traditional deco

    AI
    6 min read

  2. TrueFoundry Open-Sources TrueForge Agent Harness to Cut Managed Inference Spend

    Enterprise ML infrastructure platform TrueFoundry has open-sourced its custom agent runtime, TrueForge, under the permissive MIT license. The framework is designed to orchestrate autonomous reasoning loops, tool interactions, and long-running execution across vendor-neutral LLM backends while systematically reducing token overhead and operational compute costs. According to benchmark data published by TrueFoundry, pairing TrueForge with the open-weight GLM-5.2 model completed 11 of 14 enterpris

    AI
    2 min read

  3. Context Compaction and Session Pruning in Production AI Agents: Architecture, Hierarchical Summarization, and Constraint Preservation

    Context Compaction and Session Pruning in Production AI Agents: Architecture, Hierarchical Summarization, and Constraint Preservation Long-running autonomous agents executing multi-step workflows (codebase refactoring, recursive research, multi-turn debugging, and system operations) face an inevitable physical ceiling: working context exhaustion. As an agent executes shell commands, inspects repository trees, parses LSP diagnostics, and consumes tool outputs, the active context window fills rap

    Agents
    7 min read

  4. Reinforcement Learning with Verifiable Rewards: How Programmatic Oracles Eliminate Reward Hacking in LLM Reasoning

    Post-training paradigms for large language models have undergone a fundamental architectural shift. While the initial wave of alignment relied on Reinforcement Learning from Human Feedback (RLHF) and direct preference optimization (DPO), frontier reasoning systems increasingly depend on Reinforcement Learning with Verifiable Rewards (RLVR). Traditional RLHF relies on neural reward models trained on human pairwise comparisons. These neural proxies suffer from reward overoptimization, vulnerabili

    AI
    6 min read

  5. WhiteFiber Proposes $250M Convertible Debt Offering to Expand AI Data Center Capacity

    AI infrastructure provider WhiteFiber announced a proposed private placement of $250 million in convertible senior notes due 2032, with an option for initial purchasers to acquire up to an additional $37.5 million in notes. The proceeds are designated to fund data center campus acquisitions, facility buildouts, utility interconnection agreements, and hardware procurement for the company's AI cloud business. The financing coincides with WhiteFiber's agreement to acquire two industrial sites in Y

    AI
    2 min read

  6. Hyve Solutions Selects Nevada for Dual AI Server Manufacturing Campuses

    Hyve Solutions, the rack-scale server design and manufacturing subsidiary of TD SYNNEX, announced plans to construct two advanced manufacturing facilities in Nevada to expand domestic production of compute, storage, and networking systems for AI data centers. The development encompasses a 624,000-square-foot flagship campus in Reno alongside a secondary facility in North Las Vegas, with the combined projects projected to create approximately 3,000 jobs. The project follows formal approval of ta

    AI
    2 min read

  7. Human-in-the-Loop Architectures for Production AI Agents: Interrupt Patterns, State Resumption, and Permission Escalation

    Deploying autonomous AI agents into production environments exposes a fundamental tension between system velocity and operational safety. While read-only tasks such as data extraction and document summarization carry minimal operational blast radius, agents equipped with write-access tools (database mutations, API transactions, cloud infrastructure provisioning, and outbound communications) introduce severe operational risks. Hallucinations, prompt injections, and logical drift can trigger irrev

    AI
    6 min read

  8. Machine Unlearning in Large Language Models: How Gradient Ascent, Representation Misdirection, and Negative Alignment Purge Neural Memory

    Large language models memorize massive amounts of training data, including copyrighted literature, personally identifiable information (PII), proprietary codebases, and dual-use knowledge spanning cyber exploits and biological hazards. When copyright holders, regulators, or safety researchers demand the removal of specific data, complete retraining from scratch is economically infeasible, often costing tens of millions of dollars in compute. Machine unlearning aims to remove the influence of ta

    AI
    7 min read

  9. Developers Deploy Open-Source Workarounds to Strip Claude's Statistical Text Watermark

    Days after Anthropic introduced global text watermarking for Claude to comply with the European Union's AI Act transparency requirements, open-source developers and independent researchers have released multiple tools and pipelines aimed at stripping or perturbing the embedded statistical signatures. The rapid emergence of evasion techniques underscores the structural challenges of applying robust watermarking to natural language generation without introducing perceptible latency, semantic dist

    Anthropic
    2 min read

  10. Nvidia Discusses Investment in AI Data Supplier Mercor at $20B Valuation

    Nvidia is in discussions to participate in a funding round for AI training data marketplace Mercor that would value the three-year-old startup at $20 billion, according to reporting from The Information and Bloomberg. The transaction would double Mercor's valuation from its $10 billion Series C round closed in October 2025. It also signals an expanding capital allocation strategy from Nvidia, moving beyond compute infrastructure and cloud hardware into the upstream data curation layers powering

    Nvidia
    2 min read