Hirschberg's algorithm (1975) reduces the space complexity for the longest common subsequence problem from $O(N^2)$ to $O(N)$ via recursive midpoint bisection on a grid dynamic program (DP). We show that the underlying idea generalizes to a broad class of dynamic programs with local dependencies on directed acyclic graphs (DP DAGs). Modeling a DP as deterministic time evolution over a topologically ordered DAG with frontier width $ω$ and bounded in-degree, and assuming a max-type semiring with deterministic tie breaking, we prove that in a standard offline random-access model any such DP admits deterministic traceback in space $O(ω\log T + (\log T)^{O(1)})$ cells over a fixed finite alphabet, where $T$ is the number of states. Our construction replaces backward dynamic programs by forward-only recomputation and organizes the time order into a height-compressed recursion tree whose nodes expose small "middle frontiers'' across which every optimal path must pass. The framework yields near-optimal traceback bounds for asymmetric and banded sequence alignment, one-dimensional recurrences, and dynamic-programming formulations on graphs of bounded pathwidth. We also show that an $Ω(ω)$ space term (in bits) is unavoidable in forward single-pass models and discuss conjectured $\sqrt{T}$-type barriers in streaming settings, supporting the view that space-efficient traceback is a structural property of width-bounded DP DAGs rather than a peculiarity of grid-based algorithms.

In Autonomous Driving Systems (ADS), Directed Acyclic Graphs (DAGs) are widely used to model complex data dependencies and inter-task communication. However, existing DAG scheduling approaches oversimplify data fusion tasks by assuming fixed triggering mechanisms, failing to capture the diverse fusion patterns found in real-world ADS software stacks. In this paper, we propose a systematic framework for analyzing various fusion patterns and their performance implications in ADS. Our framework models three distinct fusion task types: timer-triggered, wait-for-all, and immediate fusion, which comprehensively represent real-world fusion behaviors. Our Integer Linear Programming (ILP)-based approach enables an optimization of multiple real-time performance metrics, including reaction time, time disparity, age of information, and response time, while generating deterministic offline schedules directly applicable to real platforms. Evaluation using real-world ADS case studies, Raspberry Pi implementation, and randomly generated DAGs demonstrates that our framework handles diverse fusion patterns beyond the scope of existing work, and achieves substantial performance improvements in comparable scenarios.

-- Social learning constitutes a fundamental framework for studying interactions among rational agents who observe each other's actions but lack direct access to individual beliefs. This paper investigates a specific social learning paradigm known as Word-of-Mouth (WoM), where a series of agents seeks to estimate the state of a dynamical system. The first agent receives noisy measurements of the state, while each subsequent agent relies solely on a degraded version of her predecessor's estimate. A defining feature of WoM is that the final agent's belief is publicly broadcast and subsequently adopted by all agents, in place of their own. We analyze this setting theoretically and through numerical simulations, noting that some agents benefit from using the belief of the last agent, while others experience performance deterioration.

The accelerated evolution of large language models has raised questions about their comparative performance across domains of practical importance. GPT-4 by OpenAI introduced advances in reasoning, multimodality, and task generalization, establishing itself as a valuable tool in education, clinical diagnosis, and academic writing, though it was accompanied by several flaws. Released in August 2025, GPT-5 incorporates a system-of-models architecture designed for task-specific optimization and, based on both anecdotal accounts and emerging evidence from the literature, demonstrates stronger performance than its predecessor in medical contexts. This study provides one of the first systematic comparisons of GPT-4 and GPT-5 using human raters from linguistics and clinical fields. Twenty experts evaluated model-generated outputs across five domains: lesson planning, assignment evaluation, clinical diagnosis, research generation, and ethical reasoning, based on predefined criteria. Mixed-effects models revealed that GPT-5 significantly outperformed GPT-4 in lesson planning, clinical diagnosis, research generation, and ethical reasoning, while both models performed comparably in assignment assessment. The findings highlight the potential of GPT-5 to serve as a context-sensitive and domain-specialized tool, offering tangible benefits for education, clinical practice, and academic research, while also advancing ethical reasoning. These results contribute to one of the earliest empirical evaluations of the evolving capabilities and practical promise of GPT-5.

Wyze was an early player in the budget home security camera game, but it's been slow to adopt 4K video resolution--until now, anyway. Available starting today, the Wyze Cam Pan v4 marks the manufacturer's first 4K security cam, following in the footsteps of TP-Link's Tapo and other home security brands that have already gone the 4K route. Landing just two weeks after Wyze's dual-lens Duo Cam Pan (which packs a pair of 2K cameras), the Wyze Cam Pan v4 carries over most of the features of its predecessor, the Cam Pan v3 from 2023. Among those features are 180-degree tilt coverage and 360-degree panning, along with IP65-rated weather protection, meaning it's dust-tight and resistant to low-pressure water jets sprayed from any angle. Compatible with Alexa and Google Home (but not Apple Home), the Cam Pan v4 also offers color night vision, two-way audio (albeit with an "improved" speaker and microphone this time around), and a privacy mode that points the camera lens straight down.

Few fantasies are harder to wipe away than the romance of a clean slate. Every January, when we're twitchy with regret and self-loathing, advertisers blare, "New Year, new you," urging us to jettison our failures and start fresh. In fiction, self-reinvention is a perennial theme, often shadowed by the suspicion that it can't be done. Lately, novelists have put a political spin on the idea, counterposing hopeful acts of individual self-fashioning to the immovable weight of circumstance. Halle Butler's "The New Me" (2019), a millennial office satire, finds its temp heroine, Millie, trying to life-hack her way out of loneliness and professional drift--buy a plant, whiten her teeth, make friends, think positive.

OpenAI has claimed to have taken a "significant step" towards artificial general intelligence (AGI) with the launch of its latest upgrade to ChatGPT, but has admitted there are still "many things" missing in its quest to create a system able to do humans' jobs. The startup said its GPT-5 model, the underlying technology that will power its breakthrough AI chatbot, represents a big upgrade on its predecessors in areas such as coding and creative writing – and is also a lot less sycophantic. It said the upgrade was being made available to all of ChatGPT's 700 million weekly users immediately. Sam Altman, OpenAI's chief executive, called the model a "significant step forward" to achieving the theoretical state of AGI, which the startup defines as a highly autonomous system that outperforms humans at most economically valuable work – or, in other words, can do their jobs. However, Altman admitted GPT-5 had not reached that goal yet.

Large reasoning models (LRMs) generate complex reasoning traces with planning, reflection, verification, and backtracking. In this work, we introduce ReasoningFlow, a unified schema for analyzing the semantic structures of these complex traces. ReasoningFlow parses traces into directed acyclic graphs, enabling the characterization of distinct reasoning patterns as subgraph structures. This human-interpretable representation offers promising applications in understanding, evaluating, and enhancing the reasoning processes of LRMs.

Boston Dynamics is at it again, wowing us with some seriously cool robotic moves. Their latest video of Atlas, their bipedal robot, has blown up online with its mind-blowing human-like movements, including breakdancing. These impressive moves are the result of a collaboration between Boston Dynamics and the Robotics and AI Institute. Get security alerts & expert tech tips – sign up for Kurt's'The CyberGuy Report' now. Breakdancing, including the famous "coffee grinder" move, is just one of the many impressive feats Atlas can perform.

A robotics company has advanced from a backflipping robot to a side-flipping robot. Robots aren't just efficient machines anymore, they are now agile performers that can flip and jog. Take, for instance, Unitree, a Chinese robotics company that has been making headlines with its incredible G1 humanoid robot. You might have seen it dancing alongside humans or remembered its predecessor, the H1, which stunned us with a backflip using electric motors. But now, the G1 has taken things to a whole new level.