In open-ended generation, LLMs frequently fall into the "likelihood trap", marked by repetitive degeneration and vocabulary dullness, creating a discrepancy between machine-generated and human-written text. While post-hoc tail truncation (e.g., Top-$p$, Min-$p$) avoids sampling from the unreliable tail, it can over-sample from the uncalibrated head and misalign generation with human lexical preferences; fixed scalar repetition penalties likewise ignore variation in logit scale across inference steps, potentially disrupting semantic coherence. To address both limitations, we propose Variance-Calibrated Modulation (VCM), a training-free pre-decoding intervention that reshapes the probability distribution before truncation through two dynamic mechanisms: (1) Contextual Searchlight via PMI, which suppresses global stopwords while elevating context-evoked tokens, and (2) Adaptive Self-Debiasing, which uses real-time logit standard deviation for scale-invariant penalization. Across open-ended generation, factual QA, and mathematical reasoning, VCM consistently mitigates the likelihood trap. With negligible computational overhead, VCM integrates with existing decoding strategies, improving diversity, coherence, and, particularly at higher decoding temperatures, reasoning accuracy.

Previous studies have compared neural activities in the visual cortex to representations in deep neural networks trained on image classification. Interestingly, while some suggest that their representations are highly similar, others argued the opposite. Here, we propose a new approach to characterize the similarity of the decision strategies of two observers (models or brains) using decision variable correlation (DVC). DVC quantifies the image-by-image correlation between the decoded decisions based on the internal neural representations in a classification task. Thus, it can capture task-relevant information rather than general representational alignment. We evaluate DVC using monkey V4/IT recordings and network models trained on image classification tasks. We find that model-model similarity is comparable to monkey-monkey similarity, whereas model-monkey similarity is consistently lower.

Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply. Existing discriminative representations often use anonymous embedding directions, while concept-bottleneck and LLM-assisted methods attach natural-language names to features without ensuring that those definitions are reproducible or distinct from the target label. We propose an operational criterion for interpretable discriminative text representations: each coordinate should satisfy conceptual clarity, measured by chance-adjusted agreement between independent annotators applying the feature definition, and label disentanglement, meaning the feature should not merely paraphrase the prediction target. We instantiate this criterion in LLM-assisted Feature Discovery (LFD), an iterative method that proposes lexical and semantic features from contrastive outcome-opposed text pairs, screens candidates using cross-LLM Cohen's $κ$, and selects features by residual held-out predictive gain. A stylized analysis connects the $κ$ screen to a per-feature annotation-noise bound, formalizing agreement as a reliability check. Across ten text-classification tasks spanning seven corpora, LFD matches the predictive performance of a strong text bottleneck baseline while producing substantially clearer and less label-entangled features. Human audits with 232 raters show that LFD features achieve higher human--human and human--LLM agreement than baseline concepts, and raters consistently judge them as less label-leaking. These results suggest that agreement-tested, label-disentangled coordinates provide a practical auditability standard for interpretable text classification.

GameStop has built up a stake of 5% in eBay and is offering to acquire the company at $125 a share. GameStop has built up a stake of 5% in eBay and is offering to acquire the company at $125 a share. GameStop's $55.5bn bid for eBay rejected as'neither credible nor attractive' Online marketplace takes into account uncertainty around US video games retailer's financing proposal The board of eBay has rejected the US video games retailer GameStop's surprise $55.5bn bid (£41bn) for the online marketplace, describing the proposal as "neither credible nor attractive". Earlier this month, GameStop made an unsolicited bid for eBay, publishing a letter on its website outlining a half-cash, half-stock proposal. This was despite the US games company - which became a global household name during the meme stock craze of 2021 - being worth far less than its takeover target.

GameStop's CEO said he could turn eBay into something worth hundreds of billions of dollars. GameStop's CEO said he could turn eBay into something worth hundreds of billions of dollars. GameStop makes $55.5bn takeover offer for eBay Video game retailer's CEO warns that unsolicited bid could turn hostile if it is rebuffed by resale site's board US video games retailer GameStop has offered to buy eBay for $55.5bn (£41bn) in an unsolicited bid that its boss warned could turn hostile if the proposal is rebuffed by eBay's board. GameStop, which has quietly accumulated a 5% stake in eBay, said it was willing to pay $125 a share, split 50-50 between cash and stock. It is an ambitious move by the games company, which catapulted to fame during the meme-stock craze of 2021 but is worth far less than its takeover target.

Video game retail chain GamesStop confirmed to the BBC on Sunday that it is making a $56bn (£41bn) unsolicited takeover offer for e-commerce firm eBay. GameStop's chief executive Ryan Cohen told the Wall Street Journal that he sees potential to make eBay a much bigger rival to Amazon, worth hundreds of billions of dollars. Cohen said his company has built a stake of around 5% in eBay and that the cash and stock takeover offer would value eBay at $125 a share, around 20% higher than its closing price on Friday. The BBC has contacted eBay for comment. Cohen also said that GameStop has a commitment letter from TD Bank to provide around $20bn in debt to help finance the deal. There is nobody who is more qualified, based on my experience, to run the eBay business, added Cohen, who is also the co-founder of online pet-products retailer Chewy.

Things to Do in L.A. From left, Meghann Adams, Malia Cohen and Herb Morgan are running for state controller in the California primary election. California voters will choose who oversees the state's finances as incumbent Malia Cohen faces Republican Herb Morgan, a finance executive, and Meghann Adams, a school bus driver and Peace and Freedom Party member. Morgan proposes using blockchain and AI technology for real-time spending transparency, while Adams advocates corporate audits and redirecting billions toward education, housing and healthcare for working-class Californians. Cohen improved financial report timeliness but fell short on promised audits of homelessness programs, the DMV and Employment Development Department. The state's fiscal watchdog oversees the intake and outtake of public funds and audits departments across the state.

Foreachparticipantit threelabels: the "suggested" label (binary), the "seen" label (categoricalwiththree "liked" label (categoricalwiththreeclasses) foratotalof 14 dimensions.

Ninedifferentdebiasing algorithms (and a baseline) have been evaluated with this dataset using the popular ResNet-18 network[36]. CelebA contains faces of celebrities with several binary task labelsandtwoprotected labels(genderandyouth). Table 3showsthe prediction results from a biased binary classifier and its bias values using the seven metrics. Without losing generality, we consider "Sport" the positive class in the binary classifier. Following the DP formula in Appendix A.2, for the "Sport" class, thePPRfemale is 45.0% (90 /200), andPPRmale is65.0%

Users Meta's A.I. Smart Glasses Are Wreaking Havoc in Schools Across the Country. It's Only Going to Get Worse. As the discreet wearable cameras become more popular, students are saying they feel constantly watched and harassed--and professors are reshaping their classrooms in response. Joziah was tabling on campus for his peer mentor job at the end of last semester at Florida State University when he noticed something strange happening across the quad: A trio of men, wearing Meta AI glasses, were stopping every young woman who passed by and asking them for their social media contacts. "I recognized them from TikTok, because they're kind of big, especially in Miami," the 19-year-old told me.