Anthropic
1.119
101
Multi-Estimate Elicitation Does Not Uniformly Debias Language Model Judgments
Does eliciting five weighted estimates within a single response change how a language model responds to information? Across anchoring, hindsight, base-rate, conjunction, and status-quo/default tasks, we compare direct numerical answers with a multi-estimate response format, contrasting source framings, explicitly random cues, and probability-consistency constraints. We evaluate three completed configurations--gpt-6.1-sol, claude-sonnet-5.5, and Qwen3-8B (reasoning disabled, analyzed descriptively). In the first two configurations, direct elicitation yielded near-zero irrelevant-anchor indices and status-quo effects, while remaining responsive to informative sources and informed advice. Of 23 preregistered confirmatory tests, six rejected after Holm adjustment; however, all six absolute point estimates fell below their prespecified smallest effect sizes of interest without establishing equivalence. Moreover, while separate versus joint elicitation shifted conjunction judgments, interpretation is constrained by between-call noise. Ultimately, multi-estimate elicitation failed to produce uniform debiasing or response attenuation across tasks and models, though post hoc correlations between candidate spread and anchoring error motivate independent predictive validation. Materials: https://github.com/asj252/llm-judgment-studies
To Jev or Not to Jev: When Can System-1 Decision Models Replace LLM Judges?
Decision models such as TypeSafe's Jev answer typed questions about a text (yes/no, multiple choice, or an ordinal score) with calibrated probabilities in a single forward pass and without generating text, which makes them an inexpensive candidate for LLM-as-a-judge workloads. We evaluate 14 System-1 decision models, selected with a pre-stated protocol, against four generative judges (Claude Haiku 5.5, GPT-5.6 with and without reasoning, and Claude Opus 5.5 with thinking) on eleven established judge benchmarks. Five of the 14 are statistically indistinguishable from GPT-5.6 without reasoning on all five pairwise and best-of-4 benchmarks, and the two strongest, DReX-Max and d1-ultra, are significantly more accurate than Haiku on JudgeBench, LLMBar and RewardBench 2. Conditioned on a reference answer generated once per prompt, eleven models reach 90.3-93.4% on JudgeBench, statistically indistinguishable from Opus 5.5 and GPT-5.6 with reasoning, and on PPE best-of-16 every reference-guided System-1 judge matches GPT-5.6 with reasoning at roughly one-sixteenth of its cost per prompt. Several System-1 judges are better calibrated than every generative judge and correlate more strongly with human helpfulness ratings. Robustness to instructions injected into the judged response is a property of the individual model rather than of the System-1 design: an appended note to the evaluator reverses 0-5% of the correct verdicts of five models but 17-49% for the other nine. Two general System-1 leaderboards that contain no judging tasks predict judging accuracy (Pearson r = 0.95-0.96), a well-calibrated System-1 first stage matches Opus on LLMBar while escalating 17% of items, and same-size controls show that the correctness deficit of a 4B decision model is a reasoning gap rather than a size gap.
Managing Amazon Bedrock costs end to end: A playbook for FinOps and platform teams
This post presents a five-stage operating model for managing Amazon Bedrock cost: attribute every dollar to an owner, make every request observable, enforce budgets, optimize platform defaults, and operate a review cadence. Written for FinOps and platform engineering teams.
Anthropic expected to receive high ‘junk’ rating from credit agencies following IPO, report says
The new report says the AI lab will likely qualify for a high junk rating.
Preview web apps live as your coding agent builds them with Amazon Bedrock AgentCore runtime
Amazon Bedrock AgentCore Session endpoints give each runtime session an authenticated URL that forwards any HTTP method, path, and WebSocket connection to the ports you allow. In this post, you deploy a coding agent that keeps a Vite website running in its session, then watch the live site update in your browser as the agent edits files.
The biggest names in AI are backing a new way to opt out of scraping
OpenAI, Google, Microsoft, and Anthropic say they will adopt a new standard that publishers can use to opt out of being scraped by AI, as…