Harnessing Source
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Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning
Announce Type: cross Abstract: Transfer learning is a natural strategy when a target population has limited data but multiple related auxiliary sources are available. A central difficulty is source heterogeneity: auxiliary sources may not be equally useful, and their usefulness may vary in a structured, cluster-like fashion. Existing transfer-learning methods often reduce source selection to a binary informative/non-informative decision, overlooking subgroups of sources with differential...
Show HN: Tired of duct-taping access control into agent prompts. Here's the fix
Cast is an open-source harness for multi-user, multi-agent systems. Self-hosted, MIT, runs on a Mac Mini. The access rule is a sentence in the prompt.
BlueFin: Benchmarking LLM Agents on Financial Spreadsheets
arXiv:2605.30907v1 Announce Type: new Abstract: We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet workbooks in the professional finance domain. Though estimates of the global population of paying users of spreadsheet software range in the hundreds of millions -- an order of magnitude more than the estimated global population of professional developers -- comparatively fewer resources have been...
What Can Verifiable Decapsulation Tests Certify? Pass Bounds and Fault-Recognition Limits for FO-Based KEMs
arXiv:2606.04443v1 Announce Type: new Abstract: Black-box tests for Fujisaki-Okamoto decapsulation observe the sampled execution seen by the harness, whereas the reencryption computation itself is visible only through the values that reach final key derivation. We study confirmation-code-augmented KEM variants under an honest-reference harness in which the reference encapsulation fixes a hidden final-key point $\langle good,B,W\rangle$, with $W$ the confirmation witness. For a $q$-localized...
A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition
arXiv:2503.15639v2 Announce Type: replace Abstract: Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios. In such cases, the deployment of heavy models becomes impractical due to constraints on memory, computational resources, and latency. To address these challenges, we propose a novel, training-free plug-and-play framework that leverages the strengths of pre-trained text...
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
arXiv:2606.05080v1 Announce Type: new Abstract: Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts. Yet existing benchmarks for frontier models primarily evaluate either single-turn responses or short-horizon agent trajectories, failing to capture the challenges of sustained iterative improvement over extended time horizons. To address this gap, we introduce...
To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling
arXiv:2605.00737v2 Announce Type: replace Abstract: Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundant or even harmful.
‘Coal power has lost its status’: Solar power outstrips coal in US despite Trump's attacks
States won by Trump in the 2024 election accounted for 74 per cent of all solar capacity installed in the first quarter of 2026. Even as President Donald Trump boosts coal over clean energy, solar power is hitting new milestones in the US and remains the leading source of new power. Data released on 10 June by global energy think tank Ember, along with a report by the Solar Energy Industries Association (SEIA) and analytics firm Wood Mackenzie, show the continued growth of solar and decline...
Bayesian-Agent: Posterior-Guided Skill Evolution for LLM Agent Harnesses
Announce Type: new Abstract: LLM agents increasingly rely on external inference conditions: prompts, tools, memory, SOPs, skills, and harness feedback. These assets can improve task execution without changing model weights, but they are often revised by heuristic reflection or by reusing observed successes and failures as if counts alone were reliable belief. We introduce \textbf{Bayesian-Agent}, a native and cross-harness framework that treats reusable skills and SOPs as hypotheses about...