evaluation

Do ACs also give scores? [D]

Navigating NeurIPS submissions can be confusing, especially for first-timers. Many authors wonder if Area Chairs (ACs) provide scores during Phase 2, the author-reviewer discussion. While you've received your meta-review, the absence of direct AC comments is a common query. It’s standard for ACs to remain largely silent during this phase, focusing on guiding the discussion. For more on navigating conference commitments, see our article, "Missed EMNLP commitment deadline, what can be done?". Focus on addressing reviewer concerns and refining your paper.

[R] CausalVLBench: Benchmarking Visual Causal Reasoning in Large VLMs.

Structured Evaluation Pipelines to Improve Your AI Workflows

Optimize your AI workflows with Structured Evaluation Pipelines, a powerful approach for consistent and reliable model assessment. This framework, submitted by /u/rhazn, offers a clear path to identify and address performance bottlenecks, ensuring your AI investments deliver tangible results. Explore a methodology that moves beyond ad-hoc testing, fostering repeatable processes and accelerating iteration. For those considering advanced study to bolster their data science skillset, see our article, "MS in Operations Research vs Data Science," for guidance on strategic career development.

No rebuttals from neurips authors [D]

Many NeurIPS authors are experiencing frustration with a lack of reviewer responses, a sentiment echoed in recent discussions. It appears the absence of author rebuttals is surprisingly common; a significant number of submissions, including borderline papers with positive Area Chair feedback, haven't received them. This leaves authors understandably perplexed. While challenging, this situation highlights a broader issue within the peer review process. For deeper insights into related concerns, explore our article, "neurips 2026: ACs and reviewers have disappeared."

NeurIPS 2026: If the rebuttal addresses your concern, please raise your score [D]

A persistent challenge within the NeurIPS community involves reviewer scoring discrepancies: concerns adequately addressed in rebuttals are not always reflected in adjusted scores. We urge reviewers to align scores with the resolution of stated concerns, regardless of personal methodological preferences. Scientific exploration thrives on diverse perspectives, and valuing rigorous responses strengthens the peer-review process. As explored in "Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler," a focus on efficient context management is key to progress.

ARR May Meta Review[D]

Recent discussions reveal a concerning trend: a significant number of authors are experiencing a lack of engagement with ARR May meta reviews. Reports indicate submissions, including rebuttals, are going unacknowledged, raising questions about reviewer participation. This issue, highlighted by /u/Historical_Pause247, impacts authors navigating conference commitments, such as the decision between EMNLP and AACL, as explored in a related article. We encourage community discussion to understand the scope and potential solutions to this challenge.

Why Reddit Data Scientists Keep Saying Not To Use Prophet

A recurring sentiment within the Reddit data science community cautions against relying on Facebook’s Prophet for time series forecasting. This post explores why, presenting initial observations and a small experiment to understand the underlying concerns. While Prophet offers accessibility, the community often finds its limitations outweigh the benefits in more complex scenarios. For those seeking robust evaluation strategies to improve forecasting workflows, our article, "Structured Evaluation Pipelines to Improve Your AI Workflows," provides deeper insights.

You're Competing Wrong in AI (Do This Instead)

Many organizations are approaching AI adoption by directly competing with established large language models—a strategy likely to yield diminishing returns. Instead, focus on building AI-native applications tailored to specific workflows. This shift empowers teams to unlock unique value and achieve transformative gains. Explore how specialized AI solutions can elevate your data management, rather than chasing broad imitation. For a deeper understanding of potential pitfalls, see our article, "Agentic Misalignment Explained." Discover a future-focused approach to AI that delivers tangible results.

Presentation: Getting Rid of LeetCode Interviews in the World of AI

Traditional LeetCode interviews are failing to identify senior engineering talent. Daniel Doubrovkine, sharing his own experience, reveals why these algorithm-focused tests often miss the mark, even for seasoned leaders. This presentation introduces actionable frameworks for a redefined interview loop, prioritizing human judgment, system design, and practical AI collaboration – yielding far stronger hiring signals. Discover how to move beyond rote memorization and evaluate real-world problem-solving capabilities. Explore this shift further with our article, "Graph Engineering for AI Agents."

Neurips 2026 Main Track Theory Paper Tracker- Discussion Thread [D]

Navigating NeurIPS 2026 Main Track Theory paper reviews? This discussion thread explores initial review distributions, a topic often generating questions. One submitter reports a 4/3/3 score with corresponding confidence, noting a historical tendency for theory papers to receive more conservative initial evaluations. Given broader reports of potentially lower scores this cycle, the thread invites fellow theory paper authors to share their experiences—scores and confidence levels—to identify potential patterns. For further context on the review process, see our related article on "Editing NeurIPS Rebuttals."

Neurips Position Track Rebuttal and Reviews [R]

Navigating the NeurIPS Position Track rebuttal process can feel unclear, especially for first-time conference paper submitters. Receiving a 3/3/5/7 alongside reviews with actionable feedback suggests a promising opportunity for revision. The rebuttal phase allows you to directly address reviewer concerns; the Area Chair (AC) will evaluate these rebuttals alongside the original reviews to determine if your revisions adequately address the feedback. Consider referencing "Link plots/figures in NeurIPS rebuttal [R]" for practical guidance on presenting supplementary data effectively.

KDnuggets Weekly Roundup: Week of July 20, 2026

This week's KDnuggets Weekly Roundup delivers essential insights for AI professionals. Top of the list: a comparison of 5 MCP Servers optimized for high-performance agentic development. Also featured are 10 newsletters to keep you ahead of the curve, a free 5-day agentic AI course from Kaggle and Google, and a deep dive into Language Model Hallucination Evaluation using GraphEval.

Language Model Hallucination Evaluation with GraphEval

Evaluating language model hallucinations remains a critical challenge. GraphEval offers a structured approach, and we’ve simulated its principles to illuminate its practical value. This exploration details the key stages of GraphEval, providing a clearer understanding of how it can identify and mitigate these inaccuracies. By visualizing the reasoning process, GraphEval empowers users to move beyond simple accuracy checks. For a deeper dive into related challenges, see "Most RAG Hallucinations Are Extraction Errors," which highlights common error patterns in retrieval-augmented generation.

ACL ARR (May 2026)- Updating Reviewer Score post 17 July AoE Deadline? [D]

Following the ACL ARR (May 2026) cycle, authors are understandably seeking clarity regarding reviewer score updates post the July 17th AoE deadline. A community discussion highlights concerns about reviewer engagement during rebuttal phases, prompting questions about the continued ability to modify ratings and participate in meta-reviewer discussions. If you volunteered as a reviewer and are unsure of your options, explore the system’s current functionality.

Number of Submissions @ AAAI [D]

The AAAI submission window has closed, with submission number 32xxx recently logged – a reminder of the intense competition within the field. A key question remains: how can we foster greater transparency in the peer review process, particularly for withdrawn or rejected papers? Increased accountability through public reviews would benefit the entire AI research community. For those exploring submission strategies within AI alignment, our recent article, "AAAI 27 AI Alignment track [D]," offers valuable guidance.

A single AI agent conversation can look perfect and still be broken, leaders from LangChain, Conviva and CoreWeave said at VB Transform 2026

Evaluating AI agents requires a shift from scrutinizing individual conversations to analyzing user cohorts against a baseline, according to leaders from LangChain, Conviva, and CoreWeave at VB Transform 2026. The disconnect between seemingly flawless agent interactions and underlying product issues is driving this change. Teams are moving toward treating evaluation criteria as a living product specification—akin to a product requirements document—rather than a static test suite. This approach, alongside cheaper, narrower judge models, promises a more reliable path to robust AI agent performance.

Introducing ASCIITermDraw Bench | Testing the ability of VLMs to Generate and Edit ASCII [P]

Can AI truly visualize complex concepts beyond code? Introducing ASCIITermDraw-Bench, a new benchmark evaluating Vision Language Models' ability to generate and edit diagrams using simple ASCII characters. This innovative benchmark addresses a critical gap, moving beyond coding and reasoning to assess diagrammatic accuracy—a surprisingly challenging task. Featuring 80 tasks spanning network topologies to software architecture, ASCIITermDraw-Bench offers a rigorous evaluation with structural and semantic scoring. See current leaderboards, including Gemma-4-31B-IT at 73.8%, and explore the methodology on Hugging Face.

ARR 2026 Meta Review score [D]

Concerns are circulating regarding the accuracy and consistency of ARR 2026 Meta Review scores, specifically around scores of 2.66 and subsequent rounding. A user has raised concerns about potential “uninterested reviewers” and AI-generated assessments impacting overall scores. This highlights a critical need for review quality assurance within the process. Explore our analysis of upcoming NeurIPS reviews, as detailed in "NeurIPS reviews coming in soon! [D]," for further insights into the broader review landscape and potential contributing factors.

Your AI Agent Passed Every Eval. Finance Still Killed It.

A recent evaluation revealed a surprising paradox: an AI agent flawlessly passed every metric in our published harness, demonstrating impressive capabilities. However, the finance department ultimately halted its deployment. While the agent resolved issues effectively, the cost of those resolutions exceeded the expense of human counterparts—a critical factor in practical application. This highlights a crucial consideration for AI adoption, as explored further in "Kimi: Threat or menace?" Demonstrating technical success doesn’t guarantee financial viability.

short-paper at ACL/EMNLP/EACL [R]

Navigating the short-paper submission process for ACL/EMNLP/EACL can be challenging. Acceptance rates for these concise submissions often lag behind those of full-length papers, and understanding the landscape is key. We're seeking insights from anyone who has successfully had a short-paper accepted to these prestigious conferences in 2025 or 2026. Sharing your track and overall assessment would be invaluable. Recent developments, like those detailed in "Prism accidentally leaked," highlight the complexities of the AI research pipeline.

Looking for JEPA devil advocates [R]

The emergence of JEPA-like world models presents a compelling, future-focused direction for robot learning, as highlighted by recent research. While Yann LeCun’s vision is undeniably ambitious, a critical evaluation is warranted. We're seeking perspectives that challenge the current trajectory – "devil's advocates" who can identify potential downsides compared to alternative world model approaches. Are there overlooked limitations or vulnerabilities within JEPA’s framework? Explore this discussion, and consider “Are Current AI Memory Architectures Optimizing for the Wrong Abstraction?” for a deeper dive into related challenges.

TACL journal doubts [D]

Navigating the TACL review process can understandably generate questions. Submitting around June 1st for the July cycle suggests reviews may arrive within the subsequent weeks, though timelines can vary. Historically, the full TACL publication process takes several months. TACL holds considerable respect within the NLP community, viewed as a strong venue for impactful research. Its reputation reflects a rigorous review process and high publication standards. For those exploring related avenues, consider reviewing discussions around short-paper submissions at ACL/EMNLP/EACL, as detailed in a recent article.

CfP | RTCA @ NeurIPS 2026 [R]

The inaugural Real-Time Conversational Agents (RTCA) Workshop at NeurIPS 2026, December 11 or 12 in Sydney, Australia, invites submissions exploring the complexities of natural, multimodal interaction. Addressing challenges like latency and cross-modal alignment, RTCA seeks original research across speech, vision, language, and HCI. We welcome full papers, short papers, and demos—all submissions must adhere to the NeurIPS 2026 style file. Interested in related developments? See "Intuit scrapped its own AI agent architecture twice in four months" for further insights. Visit rtcaneurips26.github.io/ for details

Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation

Stripe’s new benchmark reveals a significant hurdle in the rise of AI agents: while capable of constructing Stripe integrations across key workflows, they consistently struggle with validation. This suite assesses end-to-end software engineering capabilities, highlighting critical gaps in execution, testing, and validation—particularly under production-like conditions. The findings underscore that achieving reliable agentic systems requires focused improvements beyond initial build phases. For deeper insights into a related challenge, explore "Most RAG Hallucinations Are Retrieval Failures" to understand how data retrieval impacts AI accuracy.