Does the NFL's double digit ratings decline for opener show fan fatigue? Vikings say Kyler Murray feeling'pretty good,' participating in team meetings despite serious concussion Ella Langley, distractions and Lane Kiffin: Pete Golding calls Ole Miss-LSU'high school homecoming' Mother Jones' WNBA'trans panic' story shows what happens when activism masquerades as journalism'A win is a win' says athlete who didn't let a little diarrhea keep her from finishing in first place Kansas City Chiefs' roster concerns linger as they host the Denver Broncos on Monday Night Football Florida State fires AD Michael Alford, as Mike Norvell's seat reaches a boiling point in Tallahassee Madison Beer's bid to become the NFL's next Taylor Swift hits road bump, Browns are a mess & a Daejon Love fan Israeli ambassador to US: Iran's'evil regime' will fall Ben Shapiro warns Democrats' push for AI regulation is a'coordinated operation' Senate back in session as questions remain over Mitch McConnell's return Vance warns fraudsters stealing taxpayer money: 'Go and get a job' Vance warns fraudsters stealing taxpayer money: 'Go and get a job' Mullin weighs in on Obama, Biden judges amid Trump's immigration court fights Marc Thiessen warns US is in an'AI arms race' with China Charlie Kirk monument artist'protecting it from evil' after previous statue vandalized twice Army launches'field tested' campaign The mainstream sports media goons are throwing a full-blown temper tantrum because Steve Sarkisian rushed through sideline duties after an epic comeback win over the #1 Buckeyes, but Dan is putting these whiny journalists in their place. Dan annihilates the media elites who care more about their precious 15-second PR soundbites than raw, unfiltered college football passion. We're only through week two of the 2026 college football season, and already, the College Football Playoff picture has received a significant shake-up. One of the preseason favorites, the Oregon Ducks, suffered a shocking loss on the road to Oklahoma State, in a game where they gave up 554 yards defensively and an incredible 237 yards on the ground.

Oklahoma State stuns Oregon, deals early season blow to Ducks' national title hopes Former ESPN host hypocritically calls network a'political lobbying firm' after Ted Cruz appearance Team USA survives Spain scare with fourth-quarter surge to reach FIBA Women's World Cup final It's only week two, but Kansas QB Isaiah Marshall might have completed the pass of the year WWE's Roxanne Perez earns No 1 contender spot for AAA Reina de Reinas Championship at Triplemania 34 Ted Cruz got booed so loudly during'College GameDay' interview you couldn't hear a thing he said Alabama vs Kentucky over 48.5 is the college football betting play as both offenses look strong Texas Longhorns seek revenge against top-ranked Ohio State after last season's seven-point loss OutKick's Craig Carton reveals the real reason Nike stock is in the tank: 'They're failing miserably' Kyrsten Sinema: Trump has been'tremendous' on AI data centers Charles Payne: America voted for the'reindustrialization' of the nation Charles Payne: America voted for the'reindustrialization' of the nation Israeli ambassador to US: It's always been Israel, and always will be Israel Charles Payne praises'phenomenal' blue-collar boom under Trump Ret Col John Folsom outlines Dunham House's mission for combat-wounded veterans Ret Col John Folsom outlines Dunham House's mission for combat-wounded veterans Trump signs executive order to accelerate veterans' access to benefits Steve Sarkisian's squad scored just three first-half points before rallying for historic win over the Buckeyes in Austin Urban Meyer: Ohio State Has a Lot of Question Marks | Don't @ Me with Dan Dakich And you don't know until you don't know. And what I mean by that is, is at some point Ohio State is going to get hit right in the face. It might be the first quarter. It might be the first series down there in Texas. That's when you find out about your team.

Hybrid models combining Transformers and State Space Models (SSMs) are promising for balancing performance and efficiency. However, optimizing these hybrid models, particularly by addressing the potential redundancy inherent within the Transformer components, remains a significant challenge. In this paper, we propose RAD (Redundancy-Aware Distillation), a novel framework that uses self-speculative decoding as a diagnostic tool to identify redundant attention layers within the model. These identified layers are then selectively replaced with SSM components, followed by targeted (self-)distillation. Specifically, RAD focuses knowledge transfer on the components identified as redundant, considering architectural changes and specific weight initialization strategies. We experimentally demonstrate that self-distillation using RAD significantly surpasses the performance of the original base model on mathematical and coding tasks. Furthermore, RAD is also effective in standard knowledge distillation settings, achieving up to approximately 2x faster convergence compared to baseline methods. Notably, while a baseline model distilled from a Llama-3.1 70B teacher achieves scores of 46.17 on GSM8K and 22.75 on CRUX, RAD achieves significantly higher scores of 71.27 on GSM8K and 28.25 on CRUX, even when using a much smaller Llama-3.1 8B teacher. RAD offers a new pathway for efficient optimization and performance enhancement in the distillation of hybrid models.

The most fundamental capability of modern AI methods such as Large Language Models (LLMs) is the ability to predict the next token in a long sequence of tokens, known as ``sequence modeling." Although the Transformers model is the current dominant approach to sequence modeling, its quadratic computational cost with respect to sequence length is a significant drawback. State-space models (SSMs) offer a promising alternative due to their linear decoding efficiency and high parallelizability during training. However, existing SSMs often rely on seemingly ad hoc linear recurrence designs. In this work, we explore SSM design through the lens of online learning, conceptualizing SSMs as meta-modules for specific online learning problems. This approach links SSM design to formulating precise online learning objectives, with state transition rules derived from optimizing these objectives. Based on this insight, we introduce a novel deep SSM architecture based on the implicit update for optimizing an online regression objective. Our experimental results show that our models outperform state-of-the-art SSMs, including the Mamba model, on standard sequence modeling benchmarks and language modeling tasks.

Racing car aficionados will probably send me hate mail for this, but my experience has been that there is only one case, in normal driving, where a good automatic transmission is inferior to a manual transmission. Similarly in software development: in almost every case, automatic memory management is superior to manual memory management and results in far greater programmer productivity. If you were developing desktop applications in the early years of Windows, Microsoft offered you two ways to do it: writing C code which calls the Windows API directly and managing your own memory, or using Visual Basic and getting your memory managed for you. These are the two development environments I have used the most, personally, over the last 13 years or so, and I know them inside-out, and my experience has been that Visual Basic is significantly more productive. Often I've written the same code, once in C calling the Windows API and once in Visual Basic, and C always took three or four times as much work.