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Introducing Skydive by Anything, an omnichannel, cloud-native, and model-agnostic platform that enables AI teammates to operate seamlessly across channels like Slack, email, iMessage, and CLI.
Coder Agents is a platform for running AI coding agents on enterprise-controlled infrastructure, offering centralized governance, model flexibility, and auditing capabilities.
Benzi is a coding agent that queries codebases to understand and write code, achieving 77.4% on SWE-bench Verified for under $30, and is model agnostic.
This paper introduces RAEF, a model-agnostic retrieval-augmented method for time series forecasting that improves accuracy and reduces computational overhead compared to fine-tuning approaches.
This paper proposes BREAD, a scalable baseline-referenced explanation method for diagnosing anomalies in AI-based statistical process monitoring, with theoretical guarantees showing higher faithfulness than LIME under mean-shift anomalies.
PILA reformulates LLM-native advertising as a conditional response rewriting problem, decoupling ad insertion from upstream generation via a lightweight, model-agnostic sidecar module that preserves response quality while enabling controllable ad exposure.
RIS-Kernel introduces a model-agnostic sparse attention architecture (RIS) that reduces self-attention complexity from O(N^2) to O(N log N) for long-context LLM inference, enabling operation on commodity CPU hardware without GPU acceleration.
LangChain shares analysis by FactoryAI CTO Eno Reyes on how the same code review task has wildly different prices depending on the harness used, arguing a good model-agnostic harness can improve any model.
Y Combinator interviews Dust founder Stanislas Polu about building AI teammates for the workplace, the strategy of staying model agnostic, and the challenges of building alongside rapidly advancing frontier AI labs.
This paper introduces FALCON-Discover, a post-hoc framework for discovering regions where model predictions are confidently wrong, shifting calibration focus from aggregate metrics to sample-level dangerous failures.
NVIDIA Object-Oriented Agents (NOOA) proposes a Python framework where agents are Python objects, with methods as actions and docstrings as prompts, enabling reliable, testable AI agents using familiar programming paradigms.
Microsoft is testing Moonshot's Kimi model inside Copilot, highlighting a shift where users care more about utility than the underlying AI model.
Introduces a framework for training a harness to improve task LLM capabilities in a model-agnostic and task-environment-agnostic way, with results on Terminal Bench and SWE-Bench.
Proposes Progressive Tree Drafting (PTD), a training-free, model-agnostic speculative decoding method that uses progressive tree structures and stepwise pruning to enable parallel generation of multiple draft paths, achieving up to 2x speedup on various benchmarks.
Istar Code v5.0.0 is an open-sourced deterministic coding agent kernel that ensures consistent behavior across models from 1B to 500B parameters, using explicit rules, workflow phases, and verification gates.
Cloudflare explains how to build a model-agnostic vulnerability scanning harness that treats AI models as interchangeable components, enabling cross-repo tracing and false positive reduction at scale.
Proposes exogenous dropout, a simple training intervention that randomly zeros entire exogenous channels, to improve robustness of time series forecasting models against corrupted covariates. The method matches or exceeds more complex architectures across multiple corruption types and domains.
Proposes MemDefrag, a training-free framework that uses a middle-layer tracing signal to defragment latent memory in LLMs, achieving significantly better knowledge retention than existing methods like MemoryLLM and M+.
Cognition introduces Devin Fusion, an adaptive model router that reduces cost by 35% while maintaining real frontier intelligence for agentic coding tasks.
The paper proposes DIF, a model-agnostic method for denoising implicit feedback in cold-start recommendation by using pseudo-labels from content-similar warm items and uncertainty estimation, achieving significant improvements in a billion-user video app.