Talk by Shafik Quoraishee and Joanne Song (The New York Times) on local, on-device agentic AI for mobile games. Demos include an agent that plays Space Invaders entirely on a phone—perceiving the scene, predicting aliens, and dodging bullets—and one that solves the NYT mini crossword via a backtracking constraint graph. The talk covers why run AI on-device instead of in the cloud, the tight frame budget (planning within ~16 ms without draining battery or starving the renderer), and a proposal that accessibility and difficulty be treated as a single tunable dial: an agent adjusts controls in real time based on eye gaze and input precision. Closing thesis: the future of AI in games may lie in billions of small local agents rather than centralized systems. The Times emphasizes its actual puzzles are human-made with no AI involved; these projects are experimental. # Video Identity Observation — Local Agentic Theory For Mobile Games ## Video Overview - **Title:** *Local Agentic Theory For Mobile Games — Shafik Quoraishee & Joanne Song, The New York Times* - **Format:** Conference-style presentation (referenced as a "World's Fair" talk) with two speakers: Shafik Quoraishee covering on-device agentic systems for games, and Joanne Song covering accessibility applications. - **Employer affiliation stated explicitly:** "we do work at The New York Times." ## Explicit Self-Description Evidence ### Creator Claims About NYT Games - **Human-authored puzzles:** > "Our puzzles are made by people. They're not made by AI… this true and it'll always be true." - **No AI in shipped products:** > "There's no AI in the games themselves… all our games are are pretty much AI free." Wordle Bot is explicitly called out as *not* an AI feature. - **Experimental status:** > "the work that we're going to be talking about now is experimental work" — framed as research into "solvability and other capabilities of agents," not game generation. ### Demonstrated Channel Scope (from content) - **Topics covered:** - On-device/local agentic AI vs. cloud inference (latency, privacy, offline use, personalization) - Reinforcement learning history in games (finite state machines, AlphaGo/EfficientZero) - Agent demos: an agentic Space Invaders player and a **constraint-satisfaction agent for NYT Mini Crosswords** - Mobile device constraints: memory, 16ms/60Hz frame budgets, battery/NPU limits, constraint graphs - Accessibility: WCAG 2.2/3.0 grounding, dynamic "dials" (input tolerance, step granularity), gaze tracking, focus-trap repair, live layout resizing - **Cross-reference to prior content:** The speaker mentions "the last year's World Fair talk that I did on connections" (an RL solver for Connections), noting "It's on YouTube" — evidence of an ongoing series of similar technical talks. ## Intended Audience - **Technical/AI practitioner audience:** assumes familiarity with AlphaGo, EfficientZero (speaker polls the room: "has anybody heard of EfficientZero before here?"), reinforcement learning, and finite state machines. - **Context-aware framing:** "this is the graph track, but there are graphs here in this presentation" — indicates a specialized conference track audience. - **Accessibility-minded designers/developers:** Joanne's segment addresses "How can we build a system that truly serves everyone?" ## Observable Presentation Patterns - **Balanced disclaimer-first structure:** opens with institutional disclaimers before technical content — consistent with corporate-employer speaking constraints. - **Dual-speaker complementary format:** systems/AI engineering (Shafik) paired with accessibility design (Joanne). - **Vision-oriented rhetoric:** > "The future of AI doesn't have to be one giant centralized brain. It can be billions of small local brains" — mixes concrete demos with forward-looking claims. - **Honest limitation framing:** lists open problems (speed within frame budgets, long-term memory, shared game-state language, better chips, "honest testing"). ## Notes on Inference Boundaries - Employer (NYT) is stated; **channel identity is not stated** — no explicit self-description of the publishing channel itself beyond the talk venue. - No claims about the channel's broader content strategy were made; the prior-year talk reference is the only evidence of a content series. # Delivery Observation: Local Agentic Theory For Mobile Games ## Overview Two-speaker conference talk (Shafik Quoraishee, then Joanne Song) covering on-device agentic AI and accessibility for mobile games. Speakers differ noticeably in delivery style. ## Speaker 1: Shafik Quoraishee ### Tone & Style - **Conversational, informal, and hedging**: frequent fillers and self-checks with the audience. > "So, yeah. So, a few disclaimers before we continue." > "Um, has anybody heard of EfficientZero before here? Uh, if not, it's fine." - **Rhetorical audience checks** are a recurring habit — appears repeatedly: > "Have everybody seen Space Invaders before? I I presume." > "Everybody here and just in case you aren't familiar with AI in gaming..." > "if anyone everyone's familiar with a finite state machine here, probably. I would imagine enough people are." ### Vocabulary & Framing - **Technical enumeration habit**: lists constraints/parameters in threes — "space constraints, time constraints, energy constraints"; "latency," "privacy," offline capability, "personalization." - **Concrete numeric anchors** to make abstract constraints tangible: > "a refresh rate of 60 Hz... you get a new frame every 16 ms" - **Everyday analogies** to ground concepts: > "if you're in a subway tunnel or something like that, you can still have a a game with a very smart AI" > "otherwise your phones die fast as it is and they'll die even faster" ### Structural Habits - **Upfront disclaimers/positioning**: > "Our puzzles are made by people. They're not made by AI." - **Self-reference to prior work**: > "if anyone has seen the last year's World Fair talk that I did on connections, there's a similar vibe to this." - **Explicit handoff**: > "the last thing I'll show you before I hand it over to Joanne is..." ## Speaker 2: Joanne Song ### Tone & Style - **Markedly tighter and more polished** than Speaker 1 — fewer fillers, parallel sentence structures. > "Is this experience perceivable, operable, understandable, and robust?" - **Vivid metaphors** are a recurring device: > "instead of making players play operation, our agent is a live layout auditor" > "Think of it like a real-time tug-of-war where the system is no longer fixed but tunes the game to the player" - **Playful asides**: > "But you can't have rules without breaking them." > "Although it's still in draft, WCAG 3.0, spoiler alert, is following the trends..." ### Rhetorical Patterns - **Contrast framing** — juxtaposing static vs. dynamic repeatedly: > "traditionally games live in a world of fixed state models, rigid, hand-authored" > "The agent rewrites the layout live adapting to the human and not the other way around." - **Forward-looking enumeration** to close: > "First, they have to be faster... And the real magic though will be the long-term memory... And finally, we need better chips and honest testing." - **Memorable closing formulation**: > "The future of AI doesn't have to be one giant centralized brain. It can be billions of small local brains." ## Shared Delivery Patterns - Both **repeat the core thesis** (local/on-device vs. cloud) throughout, from Shafik's "ideal world when we evolve to that place, we can offload a lot of the intelligence onto a device" to Joanne's "billions of small local brains." - Both use **live/simulated demos as evidence** (Space Invaders agent, crossword constraint agent, focus-trap simulation). - Both **concede current limitations** — Shafik: "the base intelligence of a lot of these models aren't capable yet"; Joanne: "there's still groundwork to be laid." ## Uncertainty Notes - Transcript lacks audio/video cues, so tone judgments (e.g., "snorts" marker aside) rest on text fillers and phrasing only. - Audience reaction evidence is thin (one "[applause]", one "Woo!"); audience engagement during the talk can't be assessed beyond the speakers' rhetorical checks. # Position Record: Local Agentic Theory for Mobile Games **Video:** *Local Agentic Theory For Mobile Games* — Shafik Quoraishee & Joanne Song, The New York Times **Speakers:** Shafik Quoraishee (identified in transcript by Joanne's reference "like Shafiq mentioned" and his own handoff "I'll hand it over to Joanne"); Joanne Song (introduced by name). --- ## Shafik Quoraishee — Positions & Claims ### Disclaimers about NYT Games and AI - **Puzzles are made by people, not AI**: "Our puzzles are made by people. They're not made by AI... that's true and it'll always be true." - **No AI in the games themselves**: no AI features; he notes Wordle Bot is "not an AI feature." All games are "pretty much AI free." - **The presented work is experimental** — focused on solvability and agent capabilities for playability and features, *not* generating games. ### On-device AI thesis (core claim) - Most AI infrastructure today runs **in the cloud**, which is expensive due to inference round-trips and latency. - Local/offloaded intelligence offers: - **Speed**: compression of response time to within device-frame computation. - **Privacy**: computation stays within the device's confined security zone; "not needed upstream for telemetry or anything like that." - **Availability**: works anywhere, e.g., in a subway tunnel without HTTP calls getting through. - **Personalization**: local features enable tailored game experiences. ### History of AI in games - AI in games since the **1980s**, e.g., Pac-Man ghosts as **finite state machines** — conditional, symbolic AI. - Prior reinforcement learning lineage: AlphaGo/AlphaZero through **EfficientZero / EfficientZero V2** (2024), described as state-of-the-art, "sample efficient" RL models that learn fast with less data. ### RL vs. agentic systems (stated distinction) - **RL**: training cycles change model weights to tune it to a particular game. - **Agentic systems**: agents (language models) negotiate the game using **in-context learning**; reason over game state; **no reward system to grind out**; learn via an agentic loop with tool calls and local device functionality; "much more dynamic." ### Demonstrations - **Agentic Space Invaders**: a local agentic framework agent with states of perceive → predict → decide → act → loop, running on an on-device model. - **Mini crossword constraint satisfaction agent**: uses a satisfaction graph to place words; backtracks efficiently when constraints get "too crowded or too incorrect"; uses natural language from the graph to backtrack. Qualification: "sometimes finds the wrong word." ### On-device constraints (space, time, energy) - **Space**: limited device budget for weights, compressed state history, working set/planning artifacts, and render headroom. - **Time**: at 60 Hz refresh (~16 ms per frame), agent planning must fit within the frame budget or cause visible "jank." - **Energy**: current devices aren't optimized for agentic workloads; the loop must be "very curated" for minimum energy, "otherwise your phones die fast." - A **constraint graph** with soft and hard constraint management balances the three areas — e.g., soft-constrain space, but penalize time heavily to avoid disrupting user experience. - Additional considerations: **gaze estimation models** (CNN translating gaze data to agentic behavior) and the **ARC-AGI score**, since base model intelligence isn't yet capable of "all the complex decisions" in the most intelligent future games. ### Future model landscape - Generalist models (he names **Google Seema** as a candidate agentic framework — as expressed), visual-estimation agents on phones, and entire generative AI worlds as a new agentic space. --- ## Joanne Song — Positions & Claims ### Problem statement - Traditional games use **fixed, hand-authored state models**; player choice is limited to toggles or static menus — "deterministic, predictable, and completely blind to the real-time needs." - Pitfalls in mobile games (e.g., crosswords): tiny grid cells causing mistaps (motor), invisible cell highlights (low vision), forced logins/unforgiving timers (cognitive barriers to processing speed). A "static easy mode can't fix a crossword grid that remains fundamentally blind to our players." ### Grounding in standards - Design grounded in **WCAG 2.2** four pillars: perceivable, operable, understandable, robust — "a proven global foundation to build our dynamic player states." - **WCAG 3.0** (noted as still in draft) is dropping binary pass/fail for **graded bronze/silver/gold** scoring; she argues the same graded shift should apply to gameplay. ### Dynamic accessibility dials - Core tunable dials scaling with real-time player needs: - **Input tolerance** — flexibility around physical constraints. - **Step granularity** — number of operations in a sequence, breaking down complex tasks. - "The agent's entire job is to dynamically calculate these dials as they rise and fall together, balancing the scale against the player's live context." ### Agent sensing and acting (on-device) - **Sensing**: vision models for eye gaze to detect search friction; analyzing shaky taps; handwriting recognition; focus monitoring. - **Acting**: - **Focus traps**: broken dialogs create endless keyboard/switch navigation loops; the agent can "monitor that focus path, flag the loop, and inject an exit route live on the device." - **Layout auditing**: measures the interface on the fly, catches target-size violations, dynamically resizes controls — "the agent rewrites the layout live adapting to the human and not the other way around." - Framing: "a real-time tug-of-war" — accessibility and challenge become "two ends of one dial." ### Vision and open groundwork - On-device AI can move past fixed menus to "responsive empathetic partners," but **work remains**: - Speed: plans/decisions within a **16-ms frame** to prevent stuttering. - Predictive models to preview layout changes before making them. - **Long-term memory** learning one person's unique habits. - A **shared game state language** so one agent works across games without rebuilding. - **Better chips and honest testing**: faster chips paired with real benchmarks "to prove that the agent makes things better." - Closing claim: "The future of AI doesn't have to be one giant centralized brain. It can be billions of small local brains, each running on a personal device, each shaped entirely by the individual it serves." --- ## Unresolved Tensions / Qualifications (preserved) - NYT games are **AI-free today**, while the talk advocates agentic AI capabilities — the work is explicitly framed as *experimental*, for playability, not generation. - Constraint management involves **trade-offs** (soft vs. hard penalties) rather than a single optimum. - Crossword agent **sometimes finds the wrong word** — solver reliability is imperfect. - Base model intelligence (ARC-AGI reference) is **not yet sufficient** for the most complex games. - Joanne acknowledges **groundwork still to be laid**, including that benchmarks must *prove* agents improve things — the benefit is not yet established. # Speaker & Provenance Observation Record ## Video Identification - **Title:** Local Agentic Theory For Mobile Games — Shafik Quoraishee & Joanne Song, The New York Times - **Video ID:** `01a15ca8-6526-7c9c-8939-110c10111fc5` ## Speakers & Roles | Speaker | Role | Evidence | |---|---|---| | **Shafik Quoraishee** | Co-presenter / speaker; identifies as a New York Times employee ("we do work at The New York Times"); presents the technical/agentic AI portion | Delivers opening disclaimers, history of game AI, local-device thesis, constraint framework, demo walkthroughs; explicitly hands off: *"Now I'll hand it over to Joanne to talk about accessibility."* | | **Joanne Song** | Co-presenter / speaker; presents the accessibility portion | Opens with *"let's talk about accessibility"*; references *"like Shafiq mentioned"*; delivers the closing section and thanks. | | **Audience** | Unidentified; responds to speaker questions ("Has anybody heard of EfficientZero before here?") and applause at the end | No dialogue recorded. | **Note:** Speaker attribution is inferred from the title naming the two presenters and the handoff/referencing structure in the transcript; the transcript itself does not label turn-by-turn speaker names. The spelling varies in the transcript ("Shafik" in title, "Shafiq" spoken by Joanne). ## Speaker Disclaimers & Institutional Statements (Shafik Quoraishee) - **On behalf of The New York Times:** NYT puzzles are **made by people, not AI**; *"there's no AI in the games themselves"*; Wordle Bot is explicitly **not** an AI feature. - The presented work is **experimental**, focused on solvability and on-device agent capabilities (playability features), **not game generation**. ## Referenced Sources & Third-Party Claims - **Prior talk:** The speaker references his own **last year's World Fair talk on Connections** (solver deep-dive, available on YouTube). - **Historical examples cited:** Pac-Man ghosts (finite state machines), AlphaGo/AlphaZero, Space Invaders. - **Named models/systems:** EfficientZero and EfficientZero V2 (described as sample-efficient SOTA RL models, "up to 2024"), Google "Seema" (named as a generalist agentic framework), gaze estimation CNNs, **Arc AGI score**. - **Standards cited (by Joanne Song):** **WCAG 2.2** (four pillars: perceivable, operable, understandable, robust) and **WCAG 3.0** (noted as *draft*, with graded bronze/silver/gold scoring). ## Demonstrations Shown (Self-Produced / First-Party) - Agentic Space Invaders built with the presenters' local agentic framework. - A **constraint satisfaction agent** solving NYT mini crosswords using a satisfaction/constraint graph with backtracking. - Simulation of a broken dialog/keyboard focus trap with agent-injected exit route. ## Promotional Segments - **None detected as third-party promotion.** The NYT disclaimers are institutional positioning rather than a promotional segment, and are flagged above for the record. ## Availability & Limitations - **Comment availability:** Not indicated in the supplied source material. - **Curated metadata:** None supplied beyond the title; speaker names/roles derive from title plus transcript-internal evidence. - **No timestamps** present in the transcript. \>\> Awesome. So, yes, as great introduction took us to we are going to be talking about local agentic theory for accessible mobile games. And we realize this is the graph track, but there are graphs here in this presentation. So, fret not if you're missing them. So, yeah. So, a few disclaimers before we continue. Since we we do work at The New York Times and there's a few things we have to say about our puzzles AI etc. Our puzzles are made by people. They're not made by AI. So, that's just a thing we this true and it'll always be true. There's no AI in the games themselves. So, there's no AI features. You might have seen Wordle Bot, that's not an AI feature. There's so all our games are are pretty much AI free. And the work that we're going to be talking about now is experimental work. So, it's stuff that we look at in terms of solvability and other capabilities of agents in the space that we can potentially take advantage of on a local device to to do other things than actually generate games more so things about playability and other kinds of features we can utilize agents for in an intelligent way. So, introduce a little bit of an introduction. So, I also if anyone has seen the last year's World Fair talk that I did on connections, there's a similar vibe to this. I did a deeper dive into how a solver can be built for that game. And if you're interested, you can take a look. It's on YouTube and it has a lot of interesting deeper dive specifics for RL solvers, which I'll go into in a bit as well. So, little bit of a history. Everybody here and just in case you aren't familiar with AI in gaming, which for you know, it's not the most common topic here at at the World's Fair right now, but if you are familiar with the history of it, AI has been in games for a long time. And since this is since the '80s, it's been established as a core pattern in games like Pac-Man. This is a simple version of an AI called a finite state machine. And if anybody everyone's familiar with a finite state machine here, probably. I would imagine enough people are. It's a basic AI system which tells the ghosts in the game what to do when Pac-Man gets a dot or not. But basically it is a conditional kind of symbolic AI. And then moving forward we'll talk more about RL and the advancements of where that went to. But firstly, the core thesis of what we're going to really focus on is the model of running the devices running AI on our local devices. So most AI infrastructure today runs really on the cloud and if you're running mobile application, most practical scenarios involve cloud architecture. And that that essentially is expensive because you have AI calls and inference calls that you have to make upstream and then they have to come to your device. So there's things like latency, etc. So in an ideal world when we evolve to that place, we can offload a lot of the intelligence onto a device. What that does is make a lot of the make a lot of the calls faster when we get to that world because you're not doing a round trip to the cloud to get that information. So you can actually compress that time to the time within the device frame computation. That it's private, too. So your AI computation is with on it's within the device itself. And if it's set up properly, it won't leave the device because there's some things that for the gameplay experience that you don't actually need it to leave the device. Local computation is the way to go and it stays within your confined security zone uh locally. And uh it's not needed upstream for telemetry or anything like that. Um and then it should work anywhere. So, one thing is that games uh you know, some a lot of games rely on the internet and and many they they will for a live con- connection point, but at least in in the in many cases where you need advanced AI compute, you can develop games that can work locally on your uh machine, on your mobile device, rather. And uh that's great because if you're in a subway tunnel or something like that, you can still have a a game with a very smart AI that is um building uh doing something uh without being disrupted due to um uh HTTP calls that are not getting through. Um And then it's uh again, the personalization is a big thing in general with on-device models because personalization is important. Uh and a lot of people want their game experience to be tailored to them. And in the world where we can do things um on device, we have that capability more so because the the features are local. So, now we're going to talk about agentic AI for games. And um as I'm sure everybody's familiar with AlphaGo, AlphaZero, etc. here. Um the Alpha series models. So, basically um a reinforcement learning was the way that uh games were in the past um trained in order to uh do intelligence, right? So, basically what you would do is that you would take a you take a model, uh have many iterations, and then that uh that model would then um be really tuned to a particular game. Um and that's still happening, but uh what I'm doing is setting up the kind of history in the school of thought of that uh all the way up to 2024. Um has anybody heard of EfficientZero before here? Uh if not, it's fine. EfficientZero and EfficientZero V2 are the reinforcement models uh reinforcement learning models that are state-of-the-art um and they're called sample efficient reinforcement learning models because they learn uh very fast with a lot of a lot less data than traditional reinforcement learning models do on games. Um and there's just uh so that was up to 2024 and that that uh mechanic is continuing, but it lives alongside uh our reinforcement learning and agentic game uh development. So, uh reinforcement learning, again, is on the left is a bunch of cycles that are used to train a model in order to change its weight so it becomes more intelligent and able to play a particular game. An agentic system is one where you actually have a game that is a negotiated by agents, which are essentially language models, which are using in-context learning to figure out the state of the world in the game. So, RL changes the model itself and changes the weights in the model. Um the agentic system reasons over a space in the game, um and there's no reward system to grind out. Uh the loop There's an agentic loop that's at play, which then learns the system through tool calls and other things that are local to the device and using local device functionality and adapts to a new situation. So, it's much more dynamic. So, um So, this is uh basically one example of an agentic-based game using Have everybody seen Space Invaders before? I I presume. Okay. Well, if not, you will There it is. So, this is a agentic version of Space Invaders, which um is designed to use a local agentic framework which we worked on to um do basically the basic things like kill the spaceships as they get close and avoid the bullets as the spaceships attack. And you've probably seen simulations where our models have been trained to do the same thing, but this is specifically an agentic system that we built to actually play this game. Um and then you You see the agentic states on the right. And so, this is the inside the agent itself and how the loop works. So, basically, you have a very few basic states where you're trying to perceive what's happening in this where the agent is trying to perceive what's happening in the scene, where it's trying to predict the next move of the spaceships attacking it, where it's deciding what to do next because if the bullet is too close or if the spaceship is too close, it will take a it will make some sort of action and then it will act in that way in the appropriate way and then loop again and then see the state. So, that's a very simple system, but it's it's basically a simple agent that's using an on-device model to actually execute its steps. So, some challenges in this space are space \[snorts\] is health is a challenge because on the device you have a limited amount of resources. You have a device budget where you have to fit the weights, you have to compress the state history of the agent, you have to compress the working set or the planning artifacts of the agent if you even think of the way that an agent will create the way that it navigates a system, and you have to have enough render headroom in order to actually render the game and play the game itself. So, this is basically that. And then, you also have a time constraint because time itself is budgeted in a particular way so that you are essentially using like a refresh rate of 60 Hz. So, in each frame that passes, you have to which you get a new frame every 16 ms a pound if your refresh rate is 60 Hz. You need to actually fit the agent planning within that, and if you go outside of that budget, what will happen in the agent execution, you're going to see jank and other things on mobile devices that that are very hard to pull out. And then, you have energy constraints, and energy constraints are basically battery life. So, battery life you know, the basic devi- devices these days are not optimized to um really handle agentic workloads. Uh yet, they're getting to that point where you have NPUs, neural processing units, and AI chips that are specifically designed to handle that inference, but at this point in time, um the agentic design of the loop on the device has to be very curated to do as minimum energy processing as possible, otherwise your phones die fast as it is and they'll die even faster. So essentially there's a constraint function in of this for example constraint graph that takes into account all three of these areas and so there's different ways of managing the constraints in this constraint graph so you can actually make sure that your agent is performing well. So you can handle space constraints, time constraints, energy constraints and there's some techniques that we use to optimize those constraints so that we're not we're we're penalizing over focus on one area and then we're using like soft constraining on another area so that we're not penalizing too much if we need a little extra space but if we use too much time, we want to penalize that because we don't want the agent to disrupt the user experience. So we we look at things like gaze estimation models as well because gaze is another new factor. It's been around for a while but it's evolving to the point where it can actually um be useful and an agent has to take into account gaze model data which is basically convolutional neural net which takes gaze data and then can translate it to agentic behavior. And so it just begs to mention that we need to consider things like the Arc AGI score because we also have the base intelligence of the model and the base intelligence of a lot of these models aren't capable yet of being fully cognizant of all the complex decisions that an agent can make in the most intelligent and possibly interesting games in the future but we have to essentially surmount this to get to that point. And the last thing I'll show you before I hand it over to Joanne is this is another agent that we built to solve the mini crosswords. So if you play the mini crosswords, this is called a constraint satisfaction agent which uses a satisfaction graph to figure out how to put the words in the crossword puzzle at the right place so that um that it fills the puzzle properly. And it backtracks in the puzzle and finds the right word, sometimes finds the wrong word, but the constraint graph here is optimized so that if the constraints are getting too um uh too crowded or too incorrect, then it will backtrack efficiently and the agent can use natural language from the graph to backtrack and find the correct crossword uh crossword layout. Um so basically where we now, we have a lot of different models that we are considering. So we have generalist models um like for example Google Seema, which will be the agentic framework uh agentic framework that can do a lot of different things in this space. Um we have agents that do visual estimation on the phone. Uh and then we have ins- entire generative worlds that are being built using AI. Um and that will be a whole different interesting agentic space to consider. Now I'll hand it over to Joanne to talk about accessibility. \>\> Oh, is this thing on? Yes. Um let's talk about accessibility. How can we build a system that truly serves everyone? Um like Shafiq mentioned, traditionally games live in a world of fixed state models, rigid, hand-authored. A player's choice is often limited to a toggle or a static menu. The flaw here, as you could have guessed, is that it's entirely deterministic, predictable, and completely blind to the real-time needs. But we don't need to reinvent the wheel here to battle to build the battle-tested accessible game. We ground our design in an existing international standard, WCAG 2.2. This framework evaluates interactions across four human-centric pillars. Is this experience perceivable, operable, understandable, and robust? Giving us a proven global foundation to build our dynamic player states. But you can't have rules without breaking them. Some common pitfalls where our break these standards. A mobile game like crosswords, as an example. There are motor challenges we come across where tiny grid cells can lead to mistaps, active cell highlights are invisible to low vision players, uh cognitive barriers can be forcing manual logins or timers that are unforgiving and alienating to process speed uh to processing speed needs. A static easy mode can't fix a crossword grid that remains fundamentally blind to our players. Although it's still in draft, WCAG 3.0, spoiler alert, is following the trends of dropping the binary pass/fail to a graded bronze, silver, and gold scoring. Accessibility is developing into a more diverse scale and moving away from our standard checkbox. The same shift should apply for gameplay. This scale visualizes the values that can change depending on the needs of the player. Here we have the core dials that scale depending on the player's real-time needs. If we tune for a player needing more assistance, we have input tolerance that controls the flexibility around physical constraints, making interactions more forgiving. The step granularity adjusts for the number of operations in a sequence, helping users break complex tasks and tuning to players who needs more nuance. The agent's time The agent's entire job is to dynamically calculate these dials as they rise and fall together, balancing the scale against the player's live context. With on-device models, the agent can get a real-time feel for how the player is doing. For example, we can use vision models to track eye gaze and figure out when the user might be facing search friction, analyze shaky taps for someone having a hard time with controls, meet players where they're at by allow by allowing handwriting recognition. Um focus traps. But we don't want the agent to just watch, we want it to act. We simulate on the right um well, the simulation on the right is a broken dialogue forcing a user into a keyboard trap. For a user using a switch or a keyboard instead of touch, focus order is very important. If we have a broken dialogue, tab and focus can be an endless loop with no way to navigate back. Here the agent can monitor that focus path, flag the loop, and inject an exit route live on the device. When you're navigating with limited dexterity or just fat fingers, tiny targets can mean a miss. Instead of using players instead of making players play operation, our agent is a live layout auditor. It measures the interface on the fly, catches violations, and dynamically resizes the controls. The agent rewrites the layout live adapting to the human and not the other way around. Think of it like a real-time tug-of-war where the system is no longer fixed but tunes the game to the player. Accessibility and challenge stop being treated separately and become two ends of one dial constantly tuning for the moment. Ultimately, this is uh there this is where on-device AI is heading. By building systems that can actively sense human frustrations, we can move past fixed menus and turn our devices into responsive empathetic partners. But there's still groundwork to be laid. Looking ahead for local agents to truly understand games, there's still a lot we need to build. First, they have to be faster. We need a plan we need plans and decisions within a 16-ms frame to prevent stuttering for games. We need models to predict the game so that we can see exactly what a layout change will do before they actually make it. And the real magic though will be the long-term memory learning one specific person's unique habits and needs over time. We need a shared game state language so one agent can work across multiple games instead of being rebuilt from scratch for different releases releases. And then finally, we need better chips and honest testing. Faster chips paired with real benchmarks to prove that the agent makes things better. And the future of AI doesn't have to be one giant centralized brain. It can be billions of small local brains, each running on a personal device, each shaped entirely by the individual it serves. Thank you. \>\> \[applause\] \>\> Woo! \>\> \[music\]