adaptive

FinSMART introduces a market-aligned reinforcement learning framework for financial sentiment analysis, optimizing sentiment signals with realized market outcomes and achieving a 220% improvement in cumulative trading returns over the strongest baseline.

MixQuant proposes an adaptive mixed-precision quantization framework for LLMs that handles variable memory budgets by marginalizing layer distortion over random upstream configurations, outperforming existing methods across multiple models and budgets.

This paper proposes a multi-horizon reinforcement learning approach that adaptively selects and combines temporal horizons, enabling robust adaptation to changing reward structures without manual discount factor tuning, with empirical validation in MiniGrid environments.

DevinX now supports full model selection for local sessions, including Sol, Fable 5, GLM, Kimi, and an adaptive cost-balancing option, plus reasoning effort control.

AdaJEPA introduces an adaptive latent world model that continuously updates during test-time via closed-loop model predictive control, significantly improving planning success under distribution shift.

Describes the development of an AI that remembers, adapts, and becomes more useful over time, positioning it as a real partner rather than just an assistant or tool.

HarnessX introduces a framework for self-evolving AI agent harnesses that treats the runtime harness as a first-class object, enabling automatic adaptation via trace-driven reinforcement learning. It achieves average gains of +14.5% across five benchmarks, with larger improvements for weaker models.

HarnessX is a foundry for composable, adaptive, and evolvable AI agent harnesses that uses compositional primitives and trace-driven evolution to improve agent performance. Across five benchmarks, it achieves an average gain of +14.5% (up to +44.0%), demonstrating that runtime interface evolution is a complementary lever to model scaling.

SKIM is an adaptive multi-resolution soft token compression framework that compresses procedural skills for LLMs, maintaining task performance while reducing prefill cost and latency.

AdaPLD is a training-free method that improves model-free speculative decoding by using adaptive retrieval combining lexical and semantic similarity, and constructing branched reuse hypotheses to handle continuation uncertainty, achieving up to 3.10x decoding speedup.

This paper presents CosmicFish-HRM, a compact 82.77M parameter language model with a hierarchical reasoning module that dynamically allocates reasoning compute during inference, learning when to halt based on input complexity.

Proposes CIST, a method that assigns separate sample-wise adaptive temperatures to teacher and student in knowledge distillation, producing consistently informative soft labels and relaxing rigid logit-scale matching. Experiments on vision and language tasks show consistent improvements over standard KD.

A new semantic-adaptive eviction policy for LLM prefix caches that learns token reuse patterns across different token types, achieving 1.4x-2.7x TTFT improvement over existing policies.