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Agents receive an entire unlabeled workload and must stay within fixed time, compute, and API budgets, choosing approaches like training a small model or writing reusable code. Results show substantial variance among models with similar zero‑shot scores, highlighting a gap between general capability and cost‑effective specialization.

Previously in the digestTokenCast: Forecasting Token Consumption During LLM Agent Execution29 Sept

Each task provides a code scaffold and fixed simulation environment, requiring agents to understand physics, formulate dynamics mathematically, and implement executable code. The benchmark spans domains such as fluid, cloth, and rigid body dynamics, offering a systematic way to assess agents' scientific reasoning and software engineering skills.

The system maps radiomic measurements from T1, T1GD, T2, and FLAIR sequences across three tumor regions into a shared semantic space, then evaluates transportability across cohorts and conducts a pilot LLM claim‑extraction. MGMT prediction was used as a stress test for model transport.

Previously in the digestComparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports1 Oct

The updates address vulnerabilities across core system components and popular applications, with each distro publishing its own package list. AlmaLinux updates include the real‑time kernel, while Debian patches libraries like libpng and LibreOffice. Fedora’s rollout spans from the Chromium browser to the OCaml toolchain, reflecting a coordinated effort to harden the ecosystem.

The release also introduces a new -p flag for sftp lmkdir/mkdir commands and re‑enables more frequent patch cycles due to a surge in AI‑assisted bug reports. The compression change reduces side‑channel leakage at the cost of lower compression efficiency.

Chromium’s bundled dependencies and rapid version turnover made automated packaging impractical, leading to repeated breakages and user complaints. The decision reflects broader challenges in maintaining large, fast‑moving upstream projects within source‑based distributions.

The older random module remains unsuitable for security‑critical uses, and despite documentation warnings it is still occasionally misused. The secrets API offers functions like token_bytes and token_urlsafe that draw from the operating system’s CSPRNG.

By performing superoptimization on shape‑symbolic graphs, Cleave quickly checks equivalence and introduces a Split operator with a symbolic count, enabling efficient fusion of reduction‑heavy kernels such as FlashAttention. The approach separates search spaces, making large‑scale kernel generation more tractable.

Previously in the digestRLX: A Unified Multi-Backend Tensor Compiler and Distributed Runtime in Rust30 Sept

The system keeps KV caches local on the edge and uses a dyForward executor to run contiguous layer ranges without reloading weights, reducing idle gaps and allowing heterogeneous requests to be batched efficiently. This improves latency for IoT applications that rely on large language models.

Previously in the digestFrom PyTorch to the NPU: LLM-Agent-Driven Model Conversion Across Heterogeneous Inference Runtimes24 Sept

Both use cases involve widely distributed clients that must cooperate, with holographic communication demanding substantial processing power. The architecture relies on a distributed backbone to meet heterogeneous latency and bandwidth requirements, aiming to deliver consistent quality of service across the network.

Concept drift threatens production machine learning, yet the empirical behavior of multivariate two-sample drift detectors at scale remains under-characterized. Existing benchmarks rarely address the hundreds of millions of rows and high-cardinality features typical of industrial-operational datasets. We evaluate five multi-column two-sample tests (marginal, projection-based, and kernel embedding methods) across three complementary environments: the Harvard Dataverse, a validated Failing Loudly reproduction (mean absolute error between 0.030 and 0.053), and a novel synthetic-injection benchmar…

Can authentication make memory, rather than computational hardness, the attacker's bottleneck? Contextual Chain is a lightweight continuity protocol for intermittently connected devices that share evolving physical or operational context. An honest device follows one realized history, updating a compact accumulator and fixed hash-based readiness lanes; outages cause pause or bounded rollback, not branch search. After the epoch is frozen, a fresh challenge selects one lane under a short deadline. An outsider that missed context may therefore need to prepare for many mature histories before lear…

Semi-structured tables encode rich semantic information through diverse layout elements, such as hierarchical row and column headers. Answering numerical questions over such data is challenging because it requires a joint effort of accurate structural understanding of tables, question uncertainty resolution, and complex query intents interpretation. Existing methods are rarely effective in handling the above multifaceted challenges in a holistic manner; moreover, they rely on LLMs without considering financial implications. We propose SemiBonsai, a cost-effective, LLM-powered framework featuri…